Bibliographic record
Abstract
The dataset consists of metadata of 3,467 theses. The metadata (columns) includes information about the author, the jury (supervisor, other members), the institution (university, graduate school), the content (title, abstract, discipline, subject), the year and the accessibility (embargo, open access). The metadata fields are described on the platform data.gouv.fr at the following address: https://www.data.gouv.fr/fr/datasets/theses-soutenues-en-france-depuis-1985/ The theses (lines) have been selected as follows. We built up the sample in two stages: 1. The search for "sustainable development" or “développement durable” in all metadata, including title, abstract, subject indexing, and graduate school produced 2,326 theses. 2. Additional search for entry terms and related concepts of the preferred term “sustainable development” of the UNESCO thesaurus produced 1,141 other theses. The UNESCO Thesaurus is available at the following address: https://vocabularies.unesco.org/browser/thesaurus/en/
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.
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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.017 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.526 |
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; both teacher heads agree on what is shown here.
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".