Bibliographic record
Abstract
Asseoir les parties à la table de négociation est d’abord et souvent la tâche la plus difficile pour un médiateur. Malheureusement, la documentation vouée à la médiation omet souvent l’amorce du processus. L’objectif de cet article est de mieux faire comprendre les défis à relever pour amener les parties à la table et de proposer des stratégies pour aider à surmonter cet obstacle. L’hypothèse fondamentale sur laquelle repose cet article est qu’une bonne amorce incitera les parties à s’asseoir à la table de négociation. Les parties s’engageront dans une médiation une fois qu’elles auront été convaincues – au cours de l’amorce – qu’il est possible d’en arriver à une entente. L’article présente des stratégies pour atteindre ce but.
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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 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".