Replicating Fortier's THEME System for Digital Text Analysis
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
In 1971, Paul Fortier created a computer program to save significant time in analyzing French literary theme words connected to semantic fields. The system, aptly called THEME, harnessed the capabilities of computer-generated keyword concordances with frequency and distribution calculations to create research reports for user-defined literary themes. Fortier's system represents a significant achievement for digital humanities, not only due to its impressive capabilities but also for the precedents the system created in conceptualizing the role of the computer in text analysis. This paper discusses efforts to recover the THEME system and create a working approximation of the system in Python. This effort is part of Stéfan Sinclair and Geoffrey Rockwell's Epistemologica project that seeks to recover, valorize, and interpret historical text analysis in the humanities.
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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 | 0.001 | 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) | 0.000 | 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 it