Decolonising global health evaluation: Synthesis from a scoping review
Bibliographic record
Abstract
As decolonisation awareness and activism amplifies in the mainstream masses and within academic realms across a variety of fields, the time is right to converge parallel movements to decolonise the fields of global health and evaluation by restructuring relations of dependency and domination reified through the "foreign gaze"1 or "white gaze." We conducted a review of relevant records with the following inclusion criteria-they define or advocate for the decolonisation of global health evaluation or explicate methods, policies or interventions to decolonise global health evaluation published by advocates of the decolonisation movement from both fields. These records were derived following a systematic article search by the lead autthor on Google, Google Scholar, NewsBank, and PubMed using the following keywords: "decolonising" and "global health," "evaluation," or "global health evaluation" replicating a digital search strategy utilized by scoping reviews across a variety of topics. Because the topic of interest is nascent and still emerging, the date range was not restricted. The lead author screened abstracts retrieved from the search. In total, 57 records, ranging in publication date from 1994 to 2020, were selected and charted for this review. We reviewed these records to identify socio-ecological factors that influence the decolonisation of global health evaluation, such as decolonising minds; reorienting funders and reforming funding mechanisms; and investing in sustainable capacity exchange. In doing so, we reflected on our positionality as well as our internalisation and potential reinforcement of colonial relations in the process of reporting our results. In the context of turmoil and transition due to the COVID-19 pandemic, our scoping review offers a starting point to embark on a journey first to transform and decolonise global health evaluation and then to achieve the greater goal of equity and justice.
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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 | 0.100 | 0.352 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.064 | 0.054 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".