Text Mining Methods for Social Representation Analysis in Large Corpora
Bibliographic record
Abstract
With mass text digitization (digital libraries, web, etc.), a huge amount of empirical data is now available for scientific inquiry. In social sciences and humanities, the use of statistical text mining methods to analyze these data has become unavoidable. Saadi Lahlou proposed in the mid-90s a coherent framework for the application of these methods to the study of social representation in large corpora. However, despite this initiative, text mining methods have remained marginal in this research program, partly due to a poor understanding of its methodological and theoretical assumptions. There are still many analyses which confound the software with the method. This paper presents an overview and a formalization of a statistical text mining method for the study of social representation, using Lahlou’s works as illustrations. The goal is to look into the software black box while analyzing the steps and the formal operations involved. The linguistic and methodological assumptions are made explicit and alternative algorithmic operationalizations are highlighted.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".