Rethinking corruption: a decolonial inquiry into the intersection of historical systems and anti-corruption efforts
Bibliographic record
Abstract
Abstract Objectives Corruption in the health sector is particularly harmful as it undermines equitable access to quality medicines which are essential for healthcare delivery. Despite anti-corruption efforts, success has been limited, often due to a gap between policy design and on-the-ground realities in low- and middle-income countries. This article argues that colonial and neocolonial legacies continue to shape environments where corruption thrives, necessitating a re-conceptualization of the field of anti-corruption through a decolonial lens. Methods This article provides a critical overview of the literature relevant to corruption in the health sector, and the enduring impacts of colonialism and neocolonialism. Key findings Re-conceptualizations of corruption must avoid the superficial sloganism and tokenism that have characterized the decolonization discourse in recent years. Effective anti-corruption efforts require decentring Western ideologies as the dominant framework for understanding corruption and to consider the colonial and neocolonial processes that make corruption a survival tactic in some contexts, and a part of the moral economy in others. Moreover, this approach calls for critical reflection on how corruption is defined by (neo)colonial legacies, global power structures, and neoliberal agendas. Conclusions Addressing corruption in the health sector requires a shift away from Western-centric frameworks and a deeper engagement with the colonial and neocolonial contexts that enable it. By adopting a decolonial lens, anti-corruption efforts can become more attuned to the complexities of local realities and global power structures, paving the way for more effective and equitable solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".