Regards de l’IEIM – cahier 2024-2025 : les insécurités
Bibliographic record
Abstract
La série « Regards de l’IEIM » est née en 2020, à l’initiative de l’Institut d’études internationales de Montréal (IEIM). En 2024-2025, l’IEIM a choisi de mettre en valeur le travail de la communauté étudiante de l’UQAM, en lien avec la thématique annuelle choisie par l’Institut et ses unités membres : la polycrise et les insécurités. Ce recueil rassemble les contributions issues de plusieurs champs disciplinaires qui ont été retenues dans le cadre d’un appel à propositions. Découvrez les textes de Sarah Hassnaoui, Hiruni Nathasha Fernando, Djelika Keita, Amélie Chalivet, Mohamed Anoir Zayani, Camille Laty, Sihem Attalah et Marwan Attalah, Homba Alban Bassowa, Fernanda Sigüenza-Vidal, Cécilia Philippe et Lena Trottein.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".