Addressing power asymmetries in global health: Imperatives in the wake of the COVID-19 pandemic
Bibliographic record
Abstract
Pleaseconfirmthatallheadinglevelsarerepresentedcorrectly:• The Coronavirus Disease 2019 (COVID-19AU : PleasenotethatCOVID À 19hasbeendefinedasCoro) pandemic, the Black Lives Matter and Women in Global Health movements, and ongoing calls to decolonise global health have all created space for uncomfortable but important conversations that reveal serious asymmetries of power and privilege that permeate all aspects of global health.• In this article, we, a diverse, gender-balanced group of public (global) health researchers and practitioners (most currently living in the so-called global South), outline what we see as imperatives for change in a post-pandemic world.• At the individual level (including and especially ourselves), we emphasise the need to emancipate and decolonise our own minds (from the colonial conditionings of our education), straddle and use our privilege responsibly (to empower others and avoid elite capture), and build "Southern" networks (to affirm our ownership of global health).• At the organisational level, we call for global health organisations to practice real diversity and inclusion (in ways that go beyond the cosmetic), to localise their funding decisions (with people on the ground in the driving seat), and to progressively selfdecentralise (and so, divest themselves of financial, epistemic, and political power).• And at both the individual and organisational level, we emphasise the need to hold ourselves, our governments, and global health organisations accountable to these goals, and especially for governance structures and processes that reflect a commitment to real change.• By putting a spotlight on coloniality and existing inequalities, the COVID-19 pandemic inspires calls for a more equitable world and for a decolonised and decentralised approach to global health research and practice, one that moves beyond tokenistic box ticking about diversity and inclusion into real and accountable commitments to transformative change.
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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 | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.053 |
| Scholarly communication | 0.020 | 0.031 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".