Finding Traces of Cows in the Archives and Telling Stories Differently
Bibliographic record
Abstract
Archives are more-than-human spaces, and scholars are increasingly exploring how traditional archival material can be used to understand the historical lives of animals. There are traces of animals in any archives because humans do not exist in isolation and have historically been ecologically and socially entangled with other species. There is, however, a great deal of scope to develop innovative methods for telling animals’ histories in ways that treat them as subjects, not objects. Using my PhD research into the historical problematization of cows in Kingston, Ontario, between 1838 and 1938, this article charts some of the methods I developed to better position historical animals as experiential subjects in analyses of the past. More specifically, I focus on how I found traces of cows in the Queen’s University Archives by looking at a range of municipal records, including city assessments and health documents. I also explain how I conducted a multispecies discourse analysis of those traces by using contemporary knowledge about the psychology and physiology of cows, employing map-making techniques, and crafting speculative vignettes. I conclude that tracing animals in municipal records, being sensitive to contemporary knowledge about them, and making use of creative methodological tools to visibilize their spatial and social worlds is both academically interesting and politically significant. These methods challenge the anthropomorphism typical of historical and urban analyses, consequently creating openings for different ways of telling stories. RÉSUMÉ: Les archives sont plus que des environnements humains et des études académiques explorent de plus en plus comment les archives dites tradition-nelles peuvent être étudiées pour comprendre les vies historiques des animaux. Il existe des traces d’animaux dans toutes les archives puisque les humains n’existent pas en isolement et ont historiquement été écologiquement et sociologiquement mêlés avec d’autres espèces. Il y a cependant beaucoup de possibilités de développement de méthodes innovantes pour raconter des histoires où les animaux sont les sujets principaux plutôt que des objets secondaires. Utilisant mes recherches doctorales portant sur la problématisation historique des vaches à Kingston, en Ontario, entre 1838 et 1939, cet article présente certaines des méthodes que j’ai développées afin de mieux positionner les animaux historiques comme sujets expérimentaux dans l’analyse du passé. Plus précisément, je mets l’accent sur comment j’ai trouvé des traces de vaches dans les archives de l’Université Queen’s, en consultant une gamme de documents municipaux, incluant des évaluations de la ville et des documents de santé. J’explique également comment j’ai effectué une analyse des discours multispécistes de ces traces, en utilisant des connaissances contemporaines sur la psychologie et la physiologie des vaches, employant des techniques de cartographie et en créant des vignettes spéculatives. Je conclus que les traces d’animaux dans les archives municipales, en étant sensibles aux connaissances contemporaines et en utilisant des outils méthodologiques de manière créative pour visualiser leurs environnements sociaux et leurs espaces, sont à la fois intéressantes pour la recherche académique et significative au niveau politique. Ces méthodes confrontent l’anthropomorphisme typique des analyses historiques et urbaines, créant par conséquent des ouvertures afin de raconter des histoires de manière différente.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".