Bibliographic record
Abstract
Abstract: This article examines the dynamics at play as national non‐government AIDS organizations engage in shared governance with Health Canada, where the allocation of power between them is disproportionate and periodically, where their perspectives are at odds. This study takes a qualitative approach in identifying some of the relations, discourses and institutional processes that shape the informants' experiences. Findings reveal how power disparities are managed in order to affect the distribution of resources and ultimately the governance process. Sommaire : Cet article examine les dynamiques en jeu lorsque les organisations non gouvernementales (ONG) nationales de lutte contre le SIDA participent à une gouvernance partagée avec Santé Canada, alors que la distribution de pouvoir entre ces parties est disproportionnée et que périodiquement leurs points de vue sont opposés. Cette étude adopte une approche qualitative pour identifier un certain nombre de relations, de discours et de processus institutionnels qui influencent les expériences des sujets interrogés. Les conclusions révèlent comment les disparités de pouvoir sont gérées afin d'avoir une incidence sur la distribution des ressources et, en bout de ligne, sur le processus de gouvernance.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.028 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".