Switzerland and the Transatlantic Slavery

Introduction

In the last decade, the narrative that Switzerland has nothing to do with slave trade, slavery and colonialism has been severely challenged.

between the 16th and the 19th centuries, there were a number of Swiss involved in slavery, the slave trade, and colonialism activities. Swiss trading companies, banks, city-states, family enterprises, mercenary contractors, soldiers, and private individuals participated in and profited from the commercial, military, administrative, financial, scientific, ideological, and publishing activities necessary for the creation and the maintenance of the Transatlantic slavery economy. In this project, focusing on the Caribbean Community member states, we are interested in discovering the trace of the colonial past of Switzerland.

Our primary source is the CARICOM Compilation Archive written by Hans Fässler, MA Zurich University, a historian from St.Gallen (Switzerland).

Motivation

The CCA(CARICOM Compilation Archive) archive is a single-page website with contents categorized by the colonial location. Each entry begins with an arrow. The author Hans Fassler also discussed with us the issue that the website provider is warning about the growing content of CCA. Although the archive is a very informative source about the colonial past of Switzerland, it certainly creates an obstacle for potentially interested readers to learn from it in depth. The motivation of this project is to discover the previously less known history of Switzerland and provide a framework to visualize the content of the archive in a more accessible and more interactive way.

This set of properties has been validated as relevant and valuable information by Hans Fassler. In our project, we will extract the following information about each entry in the archive:

• Person's name
• City of origin in Switzerland
• Colonial location
• Date of birth and death of the person or the active date in the location
• Colonial activities that this person was involved

As we discussed with Hans, he keeps the full content of each entry because it contains more detailed information. We would like to build the map visualization based on the information we extract. This would allow the entries to be easily understandable and interpretable since the map provides geographic information to help readers identify the places. The reader can also have the visual connection between the origin in Switzerland and the colonial locations. Also, based on the information we would like to extract, we can analyze the involvement of the Swiss in the colonial era.

Project Plan and Milestones

Base on the feedback of the midterm presentation the objectives have been revised. The material traces have been left for further work and some data analysis on the existing dataset has been suggested instead.

Step I : Information extraction with NLP tools(Stanford NER, NLTK)

Step II : Visualize the connection between Switzerland and Caribbean colonies

Step III : Highlight the material traces

Date	Task	Completion
By Week 4 (07.10)	Research on potential project ideas. Initial project ideas presentation.	✓
By Week 6 (21.10)	Study similar projects for possible methodology. Decide on primary sources for this project. Study NLP pipelines and text understanding methods that can be applied to the project.	✓
By Week 10 (25.11)	Contact the author of this archive, historian Hans Fässler, to plan a meeting about the project. Step I Work on extracting name, origin, date, places, and colonial activities from the corpus. Compile a list of slave-ships Step II Decide on how to implement the visualization of the extracted information.	✓
By Week 11 (02.12)	Step I Finish data extraction and clean the data for visualization. Step II Set up a website for visualization, mapping information based on geographic location. Look at pattern in relationship btw colonial and Swiss location over time Step III Compare names from our database with Swiss street names database (if time) Compare names from our database with Dictionary of Swiss History (if time)
By Week 12 (09.12)	Step II Work on visualization, link individual/companies colonial location with origin Add a feature to see only the items with material traces Step III Compare names from our database with Wikidata database Use web scraping to enrich the material traces if low level results with other sources (less than 20% of entities)
By Week 13 (16.12)	Step II Refine website functions based on the feedback from the historian. Overall Assess the approach of the project. Write the report.
By Week 14 (22.12)	Overall Finish project and website, final presentation

Methodology

The methodology of our project is divided into three steps: text processing, data enrichment with geographical database and data visualization and analysis.

Text processing

Natural Language processing with tools as NLTK for tokenization and Stanford NER for Named Entities recognition (NER) and BIO taggings.

The NER is a text processing method that recognize and tag words that refer to named entity. In our case we are interested in using the 'PERSON' tag for (name or last name), as well as location (city, region, country) and date. In addition, we run BIO tagging where the NE are labeled based on their position ('BEGING-INSIDE-OUTSIDE') with respect to other NE. This allows grouping of person tag into a single string. An example for both steps is given below.

('=', 'O'),
('>', 'O'),
('Jean', 'PERSON'),
('Huguenin', 'PERSON'),
('(', 'O'),
('1685–1740', 'O'),
(')', 'O'),
('from', 'O'),
('Le', 'O'),
('Locle', 'ORGANIZATION'),
('(', 'ORGANIZATION'),
('Canton', 'ORGANIZATION'),
('of', 'ORGANIZATION'),
('Neuchâtel', 'ORGANIZATION'),
(')', 'O'),

('=', 'O'),
('>', 'O'),
('Jean Huguenin', 'PERSON'),
('(', 'O'),
('1685–1740', 'O'),
(')', 'O'),
('from', 'O'),
('Le', 'O'),
('Locle ( Canton of Neuchâtel', 'ORGANIZATION'),

The NER isn't completely reliable, and we can already notice some mislabeling. The use of NER versus pattern recognition is discussed in the limitations below.

Levels of confidence

For origin, data and person we calculate an accuracy value that indicates what is the level of confidence we have in the retrieved attribute. Note that there isn't any confidence level for this property as it comes directly from the author and is unambiguous.

Origin accuracy The origin location is found according to the schemes presented above. However, multiple locations exist in the same portion of text thus the actual location that we are looking for might be further away in the text. By counting the total of Swiss cities present in the text we can compute an level of confidence inversely proportional to it.

- TO ADD : is this information relevant here? or limitations?

Overall, we extract 464 text items from the division of the initial page. On this set, 117 items have no person name or location which makes them irrelevant to other dataset (precisely 49 entries have no person defined, 16 entries where neither the person nor the location could be defined and in 52 cases the person and location are in the wrong order). At this point we are left with 327 items.

Dataset enrichment with geographical databases

We add geographical information for both colonial and Swiss location, we select the following methods. For colonial location For country we use capitals geographical coordinates, except fo United States where we select the capital of each states. 'For Swiss locations

There are 194 origin with no geographic information, which represents 84 different locations. On this 194 entries without geographical coordinates, 57 were not even defined to start

Limitations

The limitations presentation follows the methodology steps.

Text processing

Limitation of NER versus pattern recognition . The results of NER processing are not reliable for all tags. For person name, Stanford NER performance are reliable and visual inspection shows good results. However, the Stanford NER is missing a lot of locations, most of them are either not recognized or miscategorized as organizations. The limitations of the tools made it worth it to use pattern matching.

Data visualization and analysis

The complete archive has 464 items, i.e. entries about different actors. However, retrieving information such as the name and origin of the actor, as well as his activities and the location of the activities is difficult. The texts can be pretty complex and intricated, 'as were the implications of Switzerland in Black Slavery'[^1].

• David Louis Agassiz (1737­–1807), uncle of the racist and glaciologist Louis Agassiz (1807–1873), was a financier who left Switzerland for France in 1747 with his friend Jacques Necker in order to work in the Parisian branch of the Thellusson et Vernet bank (investments in colonial companies, links with the slave trade). Until 1770, David Louis Agassiz cooperated with Pourtalès of Neuchâtel via the company «Joseph Lieutaud et Louis Agassiz». Necker was to become Louis XVI’s Minister of Finance, whereas David Louis Agassiz left for Britain where he acquired a considerable fortune and anglicised his name to Arthur David Lewis Agassiz. He was naturalised by a private Act of Parliament in 1766. Agassiz dealt in cotton, silk, sugar, cocoa, coffee, tobacco, and cochineal and had business relations with France, Spain, Portugal, Italy, Germany, Belgium, Denmark, the Netherlands, Sweden, Switzerland, Russia, North and South America and the East and West Indies. In 1776, Francis Anthony Rougemont (1713–1788) from a Neuchâtel family joined the partnership under the name of «Agassiz, Rougemont et Cie.», a company which had close ties with «MM Pourtalès et Cie.» from Neuchâtel (ownership of plantations on Grenada, indiennes industry, banking). Arthur David Lewis Agassiz’s son Arthur Agassiz (1771–1866), cousin of the racist Louis Agassiz, took over the family business, and later formed a company «Agassiz, Son & Company». In 1823, Arthur Agassiz was working in Port-au-Prince (Haiti) with «Jean Robert Bernard et Cie.».

There are 194 origin with no geographic information, which represents 84 different locations that are too small cities for our method.

Through processing, the number of workable entries is reduced to TO ADD.

For example Saint-Aubin, Bournens, Bourmens are too small to be in the Swiss cities dataset.

With respect to the above issues the following ideas could be implemented to improve the accuracy:

1)

The second part that transform [^1] Hans Fassler

Links

Github repository: Colonial-heritage-in-Switzerland

Primary source: caricom archives

Secondary sources: Dictionnaire historique de la Suisse DHS, swissNAME3d