Bibliographic record
Abstract
Mener des projets de recherche auprès d’intervenants d’urgence aux vécus professionnels souffrants demande empathie et respect de la part des chercheurs, et ce, d’autant plus lors des entretiens de recherche. Cet article rend compte des réflexions de deux chercheurs sur la nature, les limites et les conséquences de cette interaction. À partir de nos vécus de recherche auprès de pompiers, d’ambulanciers-paramédics et de travailleurs sociaux, nous montrerons, premièrement, que l’entretien est une interaction qui demande d’être à l’écoute des rites d’interaction et des marqueurs de fragilisation de la présentation de soi des participants. Deuxièmement, nous nous intéresserons aux conséquences, pour le chercheur, de cette exposition répétée à la souffrance d’autrui lors des entretiens, de la retranscription, et de l’analyse. Nous conclurons par la présentation d’outils interdisciplinaires que nous avons utilisés, au mieux, pour nommer, baliser et diminuer les impacts de ces interactions d’entretien parfois fort prenantes et humainement difficiles.
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 | 0.034 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.021 | 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".