Conceptualizing corruption prevention: a systematic literature review
Bibliographic record
Abstract
Purpose Existing reviews about corruption and anti-corruption have yet to treat the subject of prevention as the main focus of inquiry. The purpose of this paper is to address this need by analyzing definitions, theoretical underpinnings, methods and sectors of interest within published academic articles. By doing so, the main objective is to clarify the theoretical and conceptual foundations of the prevention of corruption. Design/methodology/approach The research design consists of a systematic literature review, which uses a keyword-string search method across relevant databases. A qualitative and quantitative coding scheme was implemented to provide descriptive statistics. Findings Results show a need for methodological diversity, theoretical debate and a clarification of the definitional foundations of corruption prevention. Specifically, the results underline a need for more interdisciplinary collaboration between the various fields that study the issue. To this end, a conceptualization of corruption prevention is proposed, built around a two by two matrix, to synthesize existing definitions and spark scholarly debate. Practical implications This paper contributes to the field of anti-corruption on a theoretical level by highlighting the current strengths and weaknesses of the inroads made by the existing literature. Moreover, on a practical research level, this paper suggests fruitful lines of inquiry to channel a rapidly expanding field of study. Social implications This paper underlines the need for corruption prevention policymaking to take note of the broad literature emanating from multiple social science disciplines. This paper also underlines the need for policy implementation to consider the socio-historical context and definitional idiosyncrasies of corruption for policy effectiveness. Originality/value A core original contribution of this paper is to advance a definition and conceptualization of corruption prevention. Using two conceptual axes – focus and scope – prevention tools are categorized and analyzed to spark further scholarly debate.
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 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 | 0.047 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.034 | 0.026 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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; 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".