Renseignements et documents à caractère personnel à des fins de recherche: quand mœurs, éthique et droit s'entremêlent !
Bibliographic record
Abstract
(English version below) Faire de la recherche nécessite bien souvent d’avoir accès à des renseignements personnels ou à des documents qui en contiennent, parfois avec le consentement des participants de la recherche, mais parfois sans leur consentement. Or, cette collecte de renseignements ou de documents à caractère personnel suscite des questionnements parmi les chercheurs et les membres des comités d’éthique de la recherche (CÉR), plus particulièrement lorsqu’il s’agit de définir qu’est-ce qu’un renseignement personnel ou qu’est-ce qu’un document à caractère personnel. Rapidement, une pléthore de questions s’ajoute aux premières et la réflexion nous entraine dans la recherche de normes éthiques ou de lois pouvant nous guider et nous aider à définir des termes (ex. anonymisation ou dépersonnalisation) ou des rôles comme ceux des CÉR, de la Commission d’accès à l’information du Québec ou des responsables de l’accès au sein des universités, notamment dans le cas d’études faisant appel à des données déjà existantes (ex. utilisation secondaire de renseignements ou de documents). Cette conférence vise à apporter quelques informations de base et à essayer de répondre aux questions susmentionnées.(English version translated with www.DeepL.com/Translator)Conducting research often requires access to personal information or documents containing it, sometimes with the consent of the research participants, but sometimes without their consent. However, this collection of personal information or documents raises questions among researchers and members of research ethics boards (REBs), particularly when it comes to defining what is personal information or what is a personal document. Rapidly, a plethora of questions are added to the first ones and reflection leads us in the search for ethical standards or laws that can guide us and help us define terms (e. g. anonymization or depersonalization) or roles such as those of REBs, the Commission d'accès à l'information du Québec or university access officials, particularly in the case of studies using existing data (e. g. secondary use of information or documents). This conference aims to provide some background information and to try to answer the above questions.This presentation is a updated version of a conference organized by the Bureau de la conduite responsable en recherche de l'Université de Montréal which is cited in page 3 of the document.
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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 | 0.043 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.023 | 0.024 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.042 | 0.031 |
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".