Competitive Bidding and Outsourcing Decisions: Principles, Methods, Practices
Bibliographic record
Abstract
Contracting out is a widely used mode of alternative government service delivery arrangement, but the methodologies to assessing competitive bids and making contracting out decisions are historically flawed. This research project has introduced a theoretical model on contracting out by adding insights from public administration, financial management, and management accounting. Two long term competitive bidding episodes of an Australian city Council are used to empirically test the theoretical model introduced in this paper. The study finds that:a methodology ingrained in cross-disciplinary are as has a better potential to explain public sector outsourcing practices, it is imperative to change the cost management systems for competitive bid costing,especially for big-budget services, and finally, a public sector entity may benefit if it imitates private sector management and accounting practices.Academics and academic researchers, postgraduate students in Accounting and Finance, the practitioners, and especially the government Finance Officers will immensely benefit from the detailed discussions of long term bid costing and assessment mechanisms presented in this research paper.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".