Changes in government procurement: COVID-19 as an opportunity for corruption
Bibliographic record
Abstract
Purpose Explore the factors making emergency procurement more prone to corruption by advancing explanations for when rules and transparency are relaxed allowing corrupt practices to emerge. Describe institutional factors, such as corruption syndrome (Johnston, 2005, 2015) and legal system, and their impact on procurement rules changes. Design/methodology/approach A qualitative event study using publicly available data offer a timeline and explanation of government procurement control mechanisms and transparency roles in emergencies by comparing two countries. Argentina and Canada had very similar and advanced food procurement systems prior to COVID-19, but they took different stances when the pandemic broke out. Findings Legal systems and corruption syndrome are linked, where Civil Law is related to Elite Cartels (Argentina) and Common Law with Influence Markets (Canada). The study contributes to understand the role of transparency to minimize the opportunity for direct purchases (electronic trails of decisions, justifications and approvals). Judicial system's actions favor corrupt practices and are aligned with elites despite the civil society outcry. Research limitations/implications Research on corrupt practices has limited access to primary data due to fear of reprisals. Informal conversations revealing glimpses of corruption were used to identify publicly available documents. Numbers play a role in emergencies and performativity theory literature is enriched by providing an example of different interpretation of information when frameworks differ between civil society and courts. Originality/value A comparative analysis that evidences the role of pre-existing institutional and social conditions shows when emergency situations will be used as an excuse to relax procurement control and transparency mechanisms which in turn facilitate corrupt practices.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".