Equity in evaluative research focusing on health cooperation and development
Bibliographic record
Abstract
The aim of this paper is to summarize the knowledge about the different approaches to equity in publications on evaluative research in development cooperation from an intercultural lens. The special issue of the Canadian Journal of Program Evaluation, Vol 30, No 3 (2015): "Decolonizing International Development Evaluation" [1] consists of a set of seven original articles, and we consider five for the present synthesis on the main issues raised on the theme of equity in evaluative research. This special issue intends to highlight the relevance of the culture and context dimensions for the evaluation executed in the area of international development. One of the findings is the existence of several challenges for the decolonization of evaluation in international development contexts, in particular regarding culture and context.
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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 | 0.240 | 0.260 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.007 | 0.058 |
| Scholarly communication | 0.027 | 0.032 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier 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".