Bibliographic record
Abstract
In this article, I provide a critical feminist analysis of my experience in a public-private partnership of university, government, and industry in New Brunswick. The project served the economic interests of the partners, supported neo-liberal discourses framing the restructuring of public services in the province, and shaped and were shaped by dominant social relations of gender, race, and class. Although the intent of the partnership was to benefit students in the public school system, my analysis points to benefits for the project partners and larger economic, social, and political interests. Keywords: corporate partnerships, health education, alcohol education, gender Dans cet article, je propose une analyse critique féministe de mon expérience au sein d’un partenariat public-privé réunissant le monde universitaire, le gouvernement et des gens d’affaires au Nouveau-Brunswick. Le projet servait les intérêts économiques des partenaires, souscrivait aux discours néolibéraux à l’origine de la restructuration des services publics dans la province et façonnait les relations sociales dominantes de genre, d’ethnicité et de classe sociale tout en étant façonnées par elles. Même si le partenariat visait à aider les élèves du système d’enseignement public, mon analyse met en évidence les avantages du projet pour les partenaires ainsi que les intérêts économiques, sociaux et politiques en jeu. Mots clés : éducation en matière de santé, éducation sur l’alcool, partenariats du gouvernement en matière d’éducation, genre
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".