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Record W2098642099 · doi:10.5430/jms.v2n2p38

Analysis on Corruption and Collusive Behaviors in Government Procurement in a Game Theory Perspective

2011· article· en· W2098642099 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Management and Strategy · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic Procurement and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsProcurementBiddingGovernment procurementLanguage changeGovernment (linguistics)Game theoryPerspective (graphical)MicroeconomicsBusinessRent-seekingIndustrial organizationPublic economicsEconomicsMarketingPoliticsComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Government Procurement is an important measure by which the government can guide economic development direction, protect and support national (or local) industry and implement macro-economic control. This paper analyzes corruption and collusive behaviors in government procurement practice in a Game Theory perspective. First, using Willenbrock’s bidding model to analyze the rent-seeking behavior in government procurement. Second, using game model to analyze procurement officials’ corruption and collusive behavior. Third, using game model to analyze suppliers’ collusive behavior. Finally, this paper proposes some corresponding solutions in each part based on game model analysis.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.252
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it