Characterizing Asymmetries in the TenTen Corpus Family Membership: An Implicit Hierarchy in Multilingual Digital Tools
Bibliographic record
Abstract
In this work, we examine the limitations of digital tools in facilitating cross-linguistic and cross-cultural research from a humanistic perspective. Our primary objective is to draw comparisons between the TenTen corpora, assessing their degree of similarity. In order to achieve this goal, we will conduct cluster analysis on the 43 corpora within the TenTen Corpus Family using a set of parameters that characterize this family membership. This analysis pinpoints the TenTen corpora that exhibit the most similar characteristics within the family, bringing to the surface an implicit hierarchy within the Sketch Engine platform, a multilingual digital tool environment. This hierarchy is structured into four distinct clusters, definable by size, number of functional tools, versions, and Part-of-Speech (PoS) tagging. The findings of the current study call for prudence when comparing the TenTen corpora, but also suggest a way of improving a multilingual environment; the examination and establishment of connections among the TenTen corpora are imperative for a comprehensive understanding of multilingualism in Digital Humanities. Dans ce travail, nous examinons les limites des outils numériques dans la facilitation de la recherche interlinguistique et interculturelle d’un point de vue humaniste. Notre objectif principal est de comparer les corpus TenTen, en évaluant leur degré de similarité. Pour atteindre cet objectif, nous réaliserons une analyse de regroupement sur les 43 corpus de la famille des corpus TenTen en utilisant un ensemble de paramètres caractérisant cette appartenance familiale. Cette analyse identifie les corpus TenTen qui présentent les caractéristiques les plus similaires au sein de la famille, révélant une hiérarchie implicite au sein de la plateforme Sketch Engine, un environnement d’outils numériques multilingues. Cette hiérarchie est structurée en quatre groupes distincts, définis par la taille, le nombre d’outils fonctionnels, les versions et le marquage des parties du discours (PoS). Les résultats de l’étude actuelle appellent à la prudence lors de la comparaison des corpus TenTen, mais suggèrent également un moyen d’améliorer un environnement multilingue ; l’examen et l’établissement de connexions entre les corpus TenTen sont impératifs pour une compréhension complète du multilinguisme dans les Humanités Numériques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".