From demand-side to supply-side regulation of government consultants: Recent trends in three OECD countries
Bibliographic record
Abstract
IMPACTBoth practitioners and academics now grapple with concerns raised in legislatures and the media surrounding the use of consultants in the public sector. Issues around declines in public service capacity and conflict of interest have featured prominently in these concerns, along with value-for-money considerations. The relationship between consultants and government purchasers has mainly been governed through demand-side regulation designed to control government purchasing behaviour, including greater transparency and monitoring of contracts. While the USA has prohibited the use of consultants for ‘inherently governmental functions’, countries like the UK, Canada, and Australia have also aimed to create a more robust system of regulation by improving the quality of the advice, and avoiding conflict of interest through regulating the supply-side of consulting. This article discusses the recent moves adopted by these countries along this regulation trajectory, including implementing limits on the ‘revolving door’ of ex-government members transmuting into consultants immediately after leaving office, and proposals to replace the current weak certification of consultants with stronger forms of professional self-regulation and licensing.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".