Contracting out: promise and performance Quiggin, J. (2002), Contracting out: promise and performance, Economic and Labour Relations Review, 13(1), 88–204.
Bibliographic record
Abstract
The adoption of systematic programs of competitive tendering and contracting has been encouraged by claims that such programs will generate substantial savings in the cost of providing public services. In this paper, it is argued that the benefits of competitive tendering and contracting have been overestimated, and that many of the apparent benefits actually reflect transfers rather than efficiency gains. Moreover, if arrangements for competitive tendering and contracting yield an inappropriate allocation of risk, such policies can reduce welfare.. A number of case studies are presented, along with recommendations for improvements in contracting policy. Contracting out: promise and performance The practice of contracting with private firms for the provision of public services is a very old one. For example, the transport of convicts to Australia was undertaken primarily by private contractors. However, the First Fleet was effectively a public venture, being under the direct control of Governor Philip, while the Second Fleet was controlled by the contractors, paid on a fixed rate per convict. As a result of the incentive to skimp on food and medical attention, around a quarter of the convicts in the Second Fleet died, and half were unfit for work when they arrived
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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 | 0.026 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".