Unethical Issues in Twenty-First Century International Development and Global Health Policy
Bibliographic record
Abstract
Abstract Billions in development aid is provided annually by international donors in the Majority World, much of which funds health equity. Yet, common neocolonial practices persist in development that compromise what is done in the name of well-intentioned policymaking and programming. Based on a qualitative analysis of fifteen case studies presented at a 2022 conference, this research examines trends involving unethical partnerships, policies, and practices in contemporary global health. The analysis identifies major modern-day issues of harmful policy and programming in international aid. Core issues include inequitable partnerships between and representation of international stakeholders and national actors, abuse of staff and unequal treatment, and new forms of microaggressive practices by Minority World entities on low-/middle-income nations (LMICs), made vulnerable by severe poverty and instability. When present, these issues often exacerbate institutionalized discrimination, hostile work environments, ethnocentrism, and poor sustainability in development. These unbalanced systems perpetuate a negative development culture and can place those willing to speak out at risk. At a time when the world faces increased threats including global warming and new health crises, development and global health policy and practice must evolve through inclusive dialogue and collaborative effort.
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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 | 0.043 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.081 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".