Seedcorn funding – Jean Golding Institute News

Irish Financial Records from the Reign of Edward I: What Applying Data Science Techniques Can Reveal

Posted on 2 December 20252 December 2025 by kerry.turcsi

JGI Seedcorn follow on funding 2023-25: Mike Jones and Brendan Smith

Introduction

This project was a follow-on extension of the JGI-funded seed-corn initiative titled ‘Digital Humanities meets Medieval Financial Records: The Receipt Rolls of the Irish Exchequer.’ The original project, along with a subsequent paper, ‘The Irish Receipt Roll of 1301–2: Data Science and Medieval Exchequer Practice,’ focused on a single receipt roll from the 1301–2 financial year. Building on this foundation, the follow-on project aimed to enhance software and techniques across a larger collection of receipt rolls from Edward I’s reign (1272–1307), offering broader insights into medieval financial practices. However, developing the scripts and troubleshooting errors took longer than expected, which reduced the time available for more in-depth analysis. Nevertheless, we managed to develop a data processing pipeline that allowed a broad analysis of the pipe rolls.

Data

The Irish Exchequer was a government institution responsible for collecting and disbursing income within the lordship of Ireland on behalf of the English Crown. Receipt rolls documented the money received each day by the Irish Exchequer from crown officials, private individuals, and communities. The entries in the rolls consisted of heavily abbreviated Medieval Latin.

There are forty surviving receipt rolls from the reign of Edward I held at the National Archives (TNA) in London. The Virtual Record Treasury of Ireland (VRTI) has translated the rolls from Latin into English for Edward I and later reigns. They have also encoded the translations into TEI/XML (https://tei-c.org), creating a machine-readable and structured digital corpus. The translations and high-quality images of the original documents are accessible to the public on the VRTI website. We gained early access to the TEI/XML documents for Edward I’s reign, which formed the foundation of our data corpus.

Data processing pipeline

To analyse the data, it was first necessary to parse the TEI/XML files and generate comma-separated (CSV) files that could be processed by Pandas, the standard Python library for data analysis, which would then allow us to create plots and visualisations with Matplotlib and Seaborn.

Each payment given to the Irish Exchequer is called a proffer. Each row in the CSV should represent an individual proffer and should include several pieces of information, including:

The financial term. The year was divided into four terms – Michaelmas, Hilary, Easter and Trinity
The date of the proffer, e.g., ‘1286-09-30’
The day of the proffer, e.g., ‘Monday’
The source of the proffer, which is a marginal heading in the roll, e.g., ‘Limerick’
The details of the proffer, e.g., ‘From the debts of various people of Co. Limerick by James Keting: £40’
The extracted monetary offering, e.g., £40
The extracted monetary offering converted to pence, e.g., 9600.0

The pipeline consists of three stages: (1) generate a CSV for each roll; (2) categorise the proffers, for example, whether they relate to profits of justice or rents; and (3) merge all the CSV files into a single ‘mega’ file.

The development of the data processing pipeline in Python was an iterative process. The script was initially written to parse the 1301–2 roll. Although the TEI/XML encoding provided structure, not all the rolls adhered to the composition of the later receipt rolls. For instance, the earlier rolls do not record dates, and some rolls were only partially complete. Consequently, significant time was spent repeatedly refining the script to accommodate the different rolls, allowing us to establish a consistent CSV format.

Part of the iterative development involved error checking, which means verifying the total income calculated from the CSV files against the totals given by the Exchequer clerk on the original roll. Ideally, the values should be either identical or have only minor differences. If the computed total is lower, this may be due to details of the proffers being lost because of damage to the original roll. Computed totals might be higher if additional proffers were added to the roll after the clerk provided the total. Either could indicate parsing errors in the TEI/XML, and any discrepancies require investigation.

A plot showing the comparison between the total provided by the Exchequer clerk, versus the computed total. Most are matching, but there are outliers where the computed total is higher, which needed extra investigation. — *A plot showing the comparison between the total provided by the Exchequer clerk, versus the computed total.*

The error checking facilitated a productive conversation between the project and VRTI, enabling the identification of errors caused by typos in the translations and markup. It also highlighted interesting features in the original rolls. For example, for E 101/230/28, the computed total was significantly greater than that provided by the clerk. The archivists at the TNA re-examined the roll and postulated that membranes from other rolls had been sewn onto this roll during repairs in the Victorian period or later.

Early access to the TEI/XML documents likely meant that more errors were encountered, as not all documents had undergone the whole VTRI editorial process. This resulted in significant time being spent tracking errors, which was not anticipated when the JGI project was conceived.

Analysis and Visualisations

Limitation in scope

After the data was processed, it became possible to analyse and visualise the proffers to the Irish Exchequer. There are 40 existing rolls for the reign. However, due to resource constraints, the analysis is limited to the 21 rolls that are ‘general’ in nature, meaning those relating to proffers from various sources and for different reasons. It does not cover the specialised rolls, such as those related to taxation.

The ‘landscape’ of the rolls

One of the initial visualisations created was to understand the ‘landscape’ of the rolls, specifically what had survived and what had not. In the subsequent plot, we display for each financial year whether we have data for each financial term or whether payments were received outside of those terms. A red box with a tick indicates we have data, and a white box with a cross indicates a gap. As you can see, there are gaps in survival (1281–82, 1283–84, 1289–90, 1297–98, 1302–03, and 1303–04), as well as years with only partial survival (1284–85, 1294–95, 1304–05).

A plot showing the availability of data per financial year and terms. Most years are complete, but some, such as 1281–1282, are missing, or incomplete, like 1284–1285. — *A plot showing the availability of data per financial year and terms.*

However, even this does not provide a complete picture since 1280–1 has an incomplete entry for Michaelmas.

Annual and termly totals

Our dataset does not encompass all income received by the Crown. As noted, some years are missing or contain only partial data, and we do not include additional rolls related to specific sources of income, such as taxation. The subsequent plot depicts the total income from our available data for each financial year, not the actual income received by the Crown.

A plot showing total computed incomes recorded on the receipt rolls per financial year. Most complete years are approaching or over £5000. — *A plot showing total computed incomes recorded on the receipt rolls per financial year.*

We can break down the total income into what was received per term for each financial year. The data is presented as a heatmap, with the darker colours indicating a greater amount of income received. Different terms received the most income in various years. For example, Michaelmas in 1285–86, 1286–87, 1288–89; Easter in 1282–83, 1291–92, 1292–93, 1301-02; and Trinity in 1306–07.

A heatmap showing the total income per financial term and year. Different years have different terms providing the most income. For example, Michaelmas for 1285–1286, 1286–1287 — *A heatmap showing the total income per financial term and year.*

The following plot shows the number of proffers received as a percentage of the total extant proffers for each financial year.

A plot showing the total received per term as a percentage of the year's total. — *A plot showing the total received per term as a percentage of the year’s total.*

Unlike the 1301–2 roll examined in the first project, Easter was not always the term that generated the highest income. However, similar to the 1301–2 roll, we can see in the following plot that, in terms of the number of proffers received each term as a percentage of the financial year, Michaelmas was often the busiest term.

A plot showing the number of proffers per term as a percentage of the year's total. Michaelmas is often the busiest in the number of proffers received. — *A plot showing the number of proffers per term as a percentage of the year’s total.*

Types of business

The proffers were categorised into five broad categories, namely, ‘farms and rents’, ‘profits of justice’, ‘customs’, ‘profits of escheatry, wardships, and temporalities’, and ‘other revenues’. The following plot shows the total income received per category for each financial year. By far, the greatest source of income is from the ‘profits of justice’ category.

A graph showing the income received per broad category. Profits of justice are by far the most outstanding category. — *A graph showing the income received per broad category.*

A plot showing the income received by category as a percentage. Profits of Justice accounts for over 50% of the business. — *A graph showing the income received by category as a percentage.*

Further work is required here, such as distinguishing the profits of justice into fines and amercements: a fine was a voluntary payment made to the king to gain favour or a privilege, such as obtaining a royal writ, whereas an amercement was a financial penalty imposed by the king or a court.

Sources of income

All the rolls specify the ‘source’ of a proffer, often a place, e.g., ‘Dublin.’ However, it can also refer to a group or other entity, e.g., ‘English debts of the merchants of Lucca’, or a specific cause, e.g., ‘By writ of England.’ The following plot shows the total income received per source in the dataset, for the twenty sources that recorded the most proffers. Dublin, by far, accounts for the most significant number of individual proffers.

A plot showing income from the top 20 sources. Dublin is the largest source of income, accounting for over £8,000, with Cork returning £7,000. — *A plot showing income from the top 20 sources.*

Conclusion

Like other Digital Humanities projects, this initiative relied heavily on human labour, especially from archivists and historians who translated the original Latin documents into English and encoded those translations into TEI-XML documents. Although we could process machine-readable datasets, extra effort was needed to clean the data and ensure its accuracy. This additional work was understandable, as the VRTI TEI/XML was created to support a digital edition of the receipt rolls rather than for statistical analysis. However, this limited the time available for detailed analysis, with most work focusing on understanding what was present in the datasets, their limitations due to document loss, and providing a general overview of the payments received. Nonetheless, the project demonstrated opportunities to develop and explore further research questions with additional funding and time.

The project was undertaken by Mike Jones of Research IT and Brendan Smith of the Department of History, with the assistance of Elizabeth Biggs of the Virtual Record Treasury of Ireland and Paul Dryburgh of The National Archives, UK.

Telling Tales: Building a Folk Map of St Lucia

Posted on 7 October 20252 December 2025 by kerry.turcsi

JGI Seedcorn Funding Project Blog 2024/25: Leighan Renaud

Screenshot from the prototype. Includes a video of a man and the transcript, interactive buttons at the top and a map that changes location with the video — *Figure 1: A screenshot from the prototype map*

In a research trip funded by the Brigstow Institute, a small research team and I met on the Eastern Caribbean island of St Lucia in the Summer of 2024 and spent 10 days immersed in the island’s folk culture. Our research was guided by the question: how do folk tales live in the 21st century Caribbean? We were particularly interested in learning more about the story of the ‘Ti Bolom’ (a Francophone Creole term that literally translates to ‘little man’): a child-sized spirit, often thought to be servant of the devil, that is summoned into the world to do its master’s nefarious bidding. The story has survived for generations on the island, and although the more specific details of this story shift depending on the storyteller, the moral of the story (which warns against greed) typically remains the same. Working with a local videographer and cultural consultant, we recorded a number of iterations of the Ti Bolom story, and conducted interviews about the island’s folk culture, for archival and analytical purposes.

The stories we heard were captivating. We heard from a diverse collection of people with different experiences of the island, its geography and its storytelling culture. The Ti Bolom stories exhibited a variety of cultural influences, from European Catholicism to West African folklore and beyond. The stories we heard also often made reference to very specific places in St Lucia (including villages and iconic locations) as well as occasionally pointing to connections with other neighbouring islands. This small archive of Ti Bolom stories demonstrated the fluid and embodied nature of folk stories and also suggested that there might be a mappable ‘folk landscape’ of the island.

We were interested in exploring innovative and interactive methods of digitally archiving Caribbean folk stories in such a way that honours their embodied nature, and we were curious about the potential of using folk stories as a decolonial mapping method (‘mapping from below’). JGI Seedcorn funding was secured to test whether we could build a ‘folk map’ of St Lucia. Working closely with Mark McLaren in the Research IT team, the aims of this exploratory project were to:

Investigate existing map-based storytelling approaches
Create folk map prototype(s) to demonstrate potential interfaces and functionalities
Document all findings, keeping in mind the potential for future projects (i.e. mapping stories across multiple islands)

Mark understood our decolonial vision immediately, and took a very considered and meticulous approach to building the prototype map, which features stories and interviews from four of the storytellers we recorded in St Lucia. He asked that I provide transcripts for each video, and a list of locations mentioned. Although St Lucia’s official national language is English, they speak Kwéyòl (a French-based creole language) locally, which means that some of the place names used by storytellers are not necessarily their ‘official’ names. This meant that I needed to be careful about ensuring my translations and transcriptions were correct, and I spoke with storytellers and other contacts in St Lucia to validate some of the locations that feature in the stories and interviews.

Mark helped us to test several levels of interactivity with the map. As such, when one chooses a story to view, the filming location and places mentioned are listed so that a user might click through them at their own pace. Simultaneously, when the video is playing and a place is mentioned, the map automatically moves to the new location. This function demonstrates the existence of the folk geographies we hypothesized during our original research project. Folk stories draw their own maps, demonstrating intricate webs of connections, both within the island and beyond.

There were ethical considerations that arose during the development of the map. Folk stories in the Caribbean are considered true. It is often the case that storytellers reference real people, and locations given can identify those people. This was not necessarily an issue we faced when building the prototype, but it did prompt us to consider how much and what kind of information the map should ethically include were it to be developed.

As an interactive folk-archiving method, mapping folk landscapes has the potential to be an innovative and visually arresting output resource that brings Caribbean folk cultures into dialogue with Digital Humanities, and makes these stories accessible for digital and diasporic audiences. The prototype we developed has proven the concept of a Caribbean folk landscape, and this has been pivotal as we develop a grant application for AHRC funding. Our hope is to secure funding to explore, archive and map three kinds of folk stories told across three islands in the Eastern Caribbean.

Thank you to Mark McLaren from the Research IT team for producing a prototype map.

To find out more about the project, feel free to check out our Instagram page, or contact me via email at Leighan.renaud@bristol.ac.uk

This project was talked about in the fourth episode of Bristol Data Stories. You can listen to it here.

MagMap – Accurate Magnetic Characteristic Mapping Using Machine Learning

Posted on 25 June 202527 June 2025 by kerry.turcsi

PGR JGI Seed Corn Funding Project Blog 2023/24: Binyu Cui

Introduction:

Magnetic components, such as inductors, play a crucial role in nearly all power electronics applications and are typically known to be the least efficient components, significantly affecting overall system performance and efficiency. Despite extensive research and analysis on the characteristics of magnetic components, a satisfactory first-principle model for their characterization remains elusive due to the nonlinear mechanisms and complex factors such as geometries and fabrication methods. My current research focuses on the characterization and modelling of magnetic core loss, which is essential for power electronics design. This research has practical applications in areas such as the fast charging of electric vehicles and the design of electric motors.

Traditional modelling methods have relied on empirical equations, such as the Steinmetz equation and the Jiles-Atherton hysteresis model, which require parameters to be curve-fitted in advance. Although these methods have been refined over generations (e.g., MSE and iGSE), they still face practical limitations. In contrast, data-driven techniques, such as machine learning with neural networks, have demonstrated advantages in addressing multivariable nonlinear regression problems.

Thanks to the funding and support from the JGI Institute, the interdisciplinary project “MagMap” has been initiated. This project encompasses testing platform modifications, database setup, and neural network development, advancing the characterization and modelling of magnetic core loss.

Outcome

Previously, a large-signal automated testing platform is produced to evaluate the magnetic characteristics under various conditions. Fig. 1 shows the layout of the hardware section of the testing platform and Fig. 2 shows the user interface of the software that is currently used for the testing. With the help of JGI, I have managed to update the automated procedure of the platform including the point-to-point testing workflow and the large signal inductance characterizing. This testing platform is crucial for generating the practical database for the further machine learning process as its automated function has largely increased the testing efficiency of each operating point (approx 6-8s per data point).

Labelled electrical components in a automated testing platform — *Fig. 1. Layout of the automated testing platform.*

Code instructions for the interface of the automated testing platform — *Fig. 2. User interface of the automated testing platform.*

Utilizing the current database, a Long Short-Term Memory (LSTM) model has been developed to predict core loss directly from the input voltage. The model shows a better performance in deducing the core loss than traditional empirical models such as the improved generalized Steinmetz equation. A screenshot of the code outcome is shown in Fig. 3 and an example result of the model for one material is shown in Figure 4. A feedforward neural network has been tried out as a scalar-to-scalar model to deduce the core loss directly from a series of input scalars including the magnetic

flux density amplitude, frequency and duty cycle. Despite the accuracy of the training process, there are limitations in the input waveform types. Convolutional neural networks have also been tested before using the LSTM as a sequence-to-scalar model. However, the model size is significantly larger than the LSTM with hardly any improvement in accuracy.

Code for the demo outcome of the LSTM — *Fig. 3. Demo outcome of the LSTM.*

Bar chart showing ratio of data points against relative error code loss (%) — *Fig. 4. Model performance against the ratio of validation sets used in the training.*

Future Plan:

Although core loss measurement and modelling is a key issue in industrial applications, the reason behind these difficulties is the non-linear relationship between the magnetic flux density and the magnetic field strength which is also known as the permeability of the magnetic material. The permeability of ferromagnetic is very sensitive to a series of external parameters including temperature, induced current, frequency and input waveform types. With an accurate fitting between the relationship of magnetic flux density and field strength, not only

the core loss can be precisely calculated but also the current modelling method that is used in Ansys and COMSOL can be improved.

Acknowledgement:

I would like to extend my gratitude to JGI for funding this research and for their unwavering support throughout the project. I am also deeply thankful to Dr. Jun Wang for his continuous support. Additionally, I would also like to express my appreciation to Mr. Yuming Huo for his invaluable advice and assistance with the neural network coding process.

Unveiling Hidden Musical Semantics: Compositionality in Music Ngram Embeddings

Posted on 25 June 2025 by kerry.turcsi

PGR JGI Seed Corn Funding Project Blog 2023/24: Zhijin Guo

Introduction

The overall aim of this project is to analyse music scores by machine learning. These of course are different from sound recordings of music, since they are symbolic representations of what musicians play. But with encoded versions of these scores (in which the graphical symbols used by musicians are rendered as categorical data) we have the chance to turn these instructions in various sequences of pitches, harmonies, rhythms, and so on.

What were the aims of the seed corn project?

CRIM concerns a special genre of works from sixteenth century Europe in which a composer took some pre-existing piece and adapted the various melodies and harmonies in it to create a new but related composition. More specifically, the CRIM Project is concerned with polyphonic music, in which several independent lines are combined in contrapuntal combinations. As in the case of any given style of music, the patterns that composers create follow certain rules: they write using stereotypical melodic and rhythmic patterns. And they combine these tunes (‘soggetti’, from the Italian word for ‘subject’ or ‘theme’) in stereotypical ways. So, we have the dimensions of melody (line), rhythm (time), and harmony (what we’d get if we slice through the music at each instant.

A network of musical notations — *Figure 1. An illustration of music graph, nodes are music ngrams and edges are different relations between them.* *Image generated by DALL·E*.

We might thus ask the following kinds of questions about music:

Starting from a given composition, what would be its nearest neighbour, based on any given set of patterns we might chose to represent? A machine would of course not know anything about the composer, genre, or borrowing involved in those pieces, but it would be revealing to compare what a machine might tell us about this such ‘neighbours’ in light of what a human might know about them.

What communities of pieces can we identify in a given corpus? That is, if we attempt to classify of groups works in some way based on shared features, what kinds of communities emerge? Are these communities related to Style? Genre? Composer? Borrowing?

In contrast, if we take the various kinds of soggetti (or other basic ‘words’) as our starting point, what can we learn about their context? What soggetti happen before and after them? At the same time as them? What soggetti are most closely related to them? And through this what can we say about the ways each kind of pattern is used?

Interval as Vectors (Music Ngrams)

How can we model these soggetti? Of course they are just sequences of pitches and durations. But since musicians move these melodies around, it will not work simply to look for strings of pitches (since as listeners we can recognize that G-A-B sounds exactly the same as C-D-E). What we need to instead is to model these as distances between notes. Musicians call these ‘intervals’ and you could think of them like musical vectors. They have direction (up/down) and they have some length (X steps along the scale).

Here is an example of how we can use our CRIM Intervals tools (a Python/Pandas library) to harvest this kind of information from XML encodings of our scores. There is more to it than this, but the basic points are clear: the distances in the score are translated into a series of distances in a table. Each column represents the motions in one voice. Each row represents successive time intervals in the piece (1.0 = one quarter note).

An ngram for a section of music — *Figure 2. An example of ngram: [-3, 3, 2, -2], interval as vectors.*

Link Prediction

We are interested in predicting unobserved or missing relations between pairs of ngrams in our musical graph. Given two ngrams (nodes in the graph), the goal is to ascertain the type and likelihood of a potential relationship (edge) between them, be it sequential, vertical, or based on thematic similarity.

Sequential is tuples that come near each other time. This is Large Language Model which computes ‘context’. LLM then produces the semantic information that is latent in the data.

Vertical is tuples that happen at the same time. It is ANOTHER kind of context.

Thematic is based on some measure of similarity.

Upon training, the model’s performance is evaluated on a held-out test set, providing metrics such as precision, recall, and F1-score for each type of relationship. The model achieved a prediction accuracy of 78%.

Beyond its predictive capabilities, the model also generates embeddings for each ngram. These embeddings, which are high-dimensional vectors encapsulating the essence of each ngram in the context of the entire graph, can serve as invaluable tools for further musical analysis.

Tracing Voices: A Visual Journey through Latin American Debates about Race

Posted on 18 June 202519 June 2025 by kerry.turcsi

JGI Seed Corn Funding Project Blog 2023/24: Jo Crow

I’m a historian who is keen to learn how digital tools can strengthen our analysis of the material we find in the archives. I research histories of race, racism and anti-racism in Latin America. I’m particularly interested in how ideas about race travelled across borders in the twentieth century, and how these cross-border conversations impacted on nation-state policies in the region.

The book I am currently writing investigates four international congresses that took place between the 1920s and 1950s: the First Latin American Communist Conference in Buenos Aires, Argentina (1929); the XXVII International Congress of Americanists in Lima, Peru (1939); the First Inter-American Conference on Social Security in Santiago, Chile (1942); and the Third Inter-American Indigenista Congress, in La Paz, Bolivia (1954). These were very different kinds of international meetings. but they all dedicated a significant amount of time to debating the problem of racial inequality, especially the ongoing marginalisation of indigenous peoples.

Who was at these congresses? Who spoke to whom, and what conversations did they have? Where did the conversations took place? What did the rooms look like? How were they set up? And what about the spaces outside the formal discussion sessions – the drinks receptions that delegates attended, the archaeological sites and museums they visited, the film screenings and book exhibitions they were invited to, the restaurants they frequented, the hotels they stayed in? Luckily, I have found a great variety of source materials – conference proceedings, newspaper reports, personal and institutional correspondence, memoirs of participating delegates – that help me begin to answer these questions.

Black and white photo of three delegates at the III Inter-American Indigenista Congress in La Paz. — *Photograph of three delegates at the III Inter-American Indigenista Congress in La Paz. Included in an International Labour Organization report of 1954.*

As part of my JGI seed-corn project, I’ve been able to work with two brilliant researchers: Emma Hazelwood and Roy Youdale. Emma helped me to explore the uses of digital mapping for visualising the “who” and “where” of these congresses, and Roy helped me to experiment with machine-reading. In this blog, I share a few of the things we achieved and learnt.

Digital Mapping

Emma started by inputting the data I had on the people who attended these congresses – their names, nationalities, where they travelled from – into Excel spreadsheets. She then found the coordinates of their origins using an online resource, and displayed them on a map using a coding language called Python. Below are a few of the results for Lima, 1939. The global map (Map 1) shows very clearly that this was a forum bringing together delegates from North, Central, and South America, and several countries in Europe too. We can zoom in to look more closely at the regional spread of delegates (Map 2), and further still to see what parts of Peru the Peruvian delegates came from (Map 3). For those delegates that were based in Lima – because we have their addresses – we can map precisely where in the city they or their institutions were based (Map 4).

Global map with red dots to show delegate locations and a green dot to highlight Peru — *Map 1. The global map shows very clearly that this was a forum bringing together delegates from North, Central, and South America, and several countries in Europe.*

Map of South America on the left and a zoomed in version on the right with red dots to show delegate locations and a green dot to highlight Peru — *Map 2 (left) shows a zoomed in version of the global map to see the regional spread of delegates. Map 3 (right) shows what parts of Peru the Peruvian delegates came from.*

Satellite image of Lima with different colour dots to symbolise different institute locations — *Map 4. For delegates in Lima, the satellite image maps where in the city they or their institutions were based.*

In some ways, these visualisations don’t tell me anything I didn’t already know. From the list of conference attendees I compiled, for instance, I already had a sense of the spread of the countries represented in Lima in 1939. What the maps do do, however, is tell the story of the international nature of the conference much more clearly and speedily than a list or table can. With the city map, showing where Lima-based delegates lived and worked, we do learn something new. By plotting the addresses, I can envisage the contours of the space they occupied. I couldn’t do that in my head with just a list of the addresses, especially without knowing road names.

The digital maps also help with comparative analysis. If we look at the global map (like Map 1) of all four congresses together we get a clear view of their very similar reach; most delegates to all of them were from South America. We are also able to swiftly detect the differences – for example, that the Lima conference attracted more delegates from Europe than the other meetings, or that there were no delegates from Europe at the 1954 congress in La Paz. We can then think about the reasons why.

Satellite image of Lima with an old map layered on top with different colour dots to symbolise different locations — *Map 5. Shows the main venues for the XXVII International Congress of Americanists.*

Map 5 above takes us back to Lima. It shows the main venues for the XXVII International Congress of Americanists. It visualizes a circuit for us. I don’t think we can perceive this so clearly from a list of venues, especially if we are not very familiar with the city. Here we can see that most of the conference venues and the hotels where delegates stayed were clustered quite closely together, in Lima’s historic centre. Delegates could easily walk between them. There are a few outliers, though: one of the archaeological sites that delegates visited, the museum that threw a reception for delegates, and a couple of restaurants too. This prompts further questions and encourages us to imagine the delegates moving through the city.

Machine Reading

As well as digital mapping, I’ve been keen to explore what machine or distant reading can add to our analysis of debates about race in early twentieth century Latin America. It’s widely known, for example, that, in the context of the Second World War, many academic and government institutions rejected the scientific validity of the term race (“raza” in Spanish). A machine reading of the proceedings of these four congresses gives us concrete, empirical evidence of how the word race was, in practice, used less and less from 1929, to 1939, to 1942, to 1954. Text analysis software like Sketch Engine, which Roy introduced me to, also enables us to scrutinise how the term was used when it was used. For instance, in the case of the 1929 conference in Buenos Aires, Sketch Engine processes 300+ pages of conference discussions in milliseconds and shows us in a systematic way which so-called “races” were being talked about, the fact that “race” was articulated as an object and a subject of the verb, and how delegates associated the term race with hostile relations, nationhood, indigenous communities, exploitation, and cultural tradition (see below). In short, it provides a really useful, methodical snapshot of the many different languages of race being spoken in Buenos Aires. It is then up to me to reflect on the significance of the detail, and to go back to specific moments in the text, for example the statement of one delegate about converting the “race factor” into a “revolutionary factor”.

*Results from a text analysis in Sketch Engine for the 1929 conference in Buenos Aires. The result shows us in a systematic way which so-called “races” were being talked about.*

In all, I’ve learnt how digital tools and methodologies can productively change how we’re able to look at things, in this case “race-talk” and who was speaking it. By looking differently we see differently too. What I’d like to do now is to trace where the conversations went from these congresses, and see how much they shifted and transformed in the process of travel.

Jo Crow Professor of Latin American Studies , School of Modern Languages