Concept Detection in Philosophical Corpora
Bibliographic record
Abstract
During the course of research, scholars often search large textual databases for segments of text relevant to their conceptual analyses. This study proposes, develops and evaluates two applications of word embedding algorithms for automated Concept Detection in theoretical corpora: Average Cosine Similarity Retrieval (ACS) and Word Mover’s Distance Retrieval (WMDR). Both strategies are evaluated against weighted keyword (KW) search using a test set from the Digital Ricœur corpus tagged by scholarly experts. In our experiments, WMDR outperformed weighted keyword search on the Concept Detection task, which suggests it is a promising strategy for Concept Detection and information retrieval systems focused on theoretical corpora. Besides these initial positive results, WMDRl has as its major characteristic the ability to use definitions as proxies for concepts; this provides search results that account for the semantic contexts of theoretical concepts.Au cours de la recherche, les chercheurs tâchent souvent de trouver des segments de texte pertinents dans d’énormes bases de données textuelles pour leurs analyses conceptuelles. Cet article propose, développe et évalue deux applications d’algorithmes de word embedding (plongement lexical) pour la Concept Detection (détection de concept) dans des corpus théoriques : dans l’Average Cosine Similarity Retrieval (ACS – Extraction de similitudes cosinus moyenne) et dans la Word Mover’s Distance Retrieval (WMDR – Extraction de distance de déplacement lexique). Les deux stratégies seront évaluées par rapport à une recherche par mot-clés pondérée avec un dispositif de test venant du corpus Digital Ricœur, qui est étiqueté par des experts érudits. Dans nos expériences, la WMDR était plus performante que la recherche par mot-clés pondérée durant la tâche de détection de concept, ce qui suggère que cela soit une stratégie prometteuse pour la détection de concept et pour des systèmes d’extraction d’information axés sur des corpus théoriques. En outre, la WMDR a comme caractéristique majeure la capacité de se servir de définitions en tant que des proxys pour des concepts. Cela fournit des résultats de recherche qui explique les contextes sémantiques de concepts théoriques.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".