Creating and Testing the Concept of an Academic NGO for Enhancing Health Equity: A New Mode of Knowledge Production?
Bibliographic record
Abstract
CONTEXT: Collaborative action is required to address persistent and systematic health inequities which exist for most diseases in most countries of the world. OBJECTIVES: The Academic NGO initiative (ACANGO) described in this paper was set up as a focused network giving priority to twinned partnerships between Academic research centres and community-based NGOs. ACANGO aims to capture the strengths of both in order to build consensus among stakeholders, engage the community, focus on leadership training, shared management and resource development and deployment. METHODS: A conceptual model was developed through a series of community consultations. This model was tested with four academic-community challenge projects based in Kenya, Canada, Thailand and Rwanda and an online forum and coordinating hub based at the University of Ottawa. FINDINGS: Between February 2005 and February 2007, each of the four challenge projects was able to show specific outputs, outcomes and impacts related to enhancing health equity through the relevant production and application of knowledge. CONCLUSIONS: The ACANGO initiative model and network has demonstrated success in enhancing the production and use of knowledge in program design and implementation for vulnerable populations.
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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 | 0.060 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".