Text Mining Methods for Social Representation Analysis in Large Corpora
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it