Bibliographic record
Abstract
Bonjour toutes et tous.Je commence par m'introduire dans ma langue abnakise.Je me nomme Michelle O'Bonsawin.Je suis une fire abnakise de la premire nation d'Odanak et du clan de la tortue.Je suis la fille de Richard et Diane.Je tiens tout d'abord remercier la Revue de droit de McGill de m'avoir invite m'adresser vous aujourd'hui.Je suis ravie d'tre ici.Je veux vous parler de deux sujets qui me tiennent coeur : la sant mentale et l'accs la justice.Personne ne peut nier que notre sant mentale est de plus en plus mise l'preuve.D'aprs les recherches, la sant mentale et la toxicomanie taient dj l'une des principales causes d'invalidit au Canada lorsque la COVID-19 a frapp. 1 Avec l'arrive de la pandmie, nous avons toutes et tous souffert encore plus de stress, de peur et vcu plusieurs deuils.En tant qu'tudiantes, tudiants, professeures et professeurs d'universit, je suis certaine que vous tes parfaitement conscientes et conscients de cette ralit.Votre sant mentale est un facteur important
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".