Bibliographic record
Abstract
A key challenge for central government in implementing the General Agreement on Trade in Services (GATS) to liberalise public services when it came into effect in 1996 was to find a way of market making. It would be difficult to open public services to competition where there had been no private sector engagement before. In some public services, there had been little external provision. Introducing change and potential for opening internal processes to competition meant that private sector contractors would have to be attracted to establishing a capability to bid for this work (Walsh, 1995; Héritier, 2001). This was particularly in relation to professional and specialist functions in local and central government such as building control, finance, legal services and planning. In some services, it was possible to transfer skills between the public and private sectors very readily, such as human resources, although there continued to be differences in the cultural context of the public sector (Harris, 2008). In other local authority services, the development of private sector markets was encouraged through changes in regulation such as planning (Adams and Tiesdell, 2010; RTPI, 2019). There were also challenges about the ways public bodies defined their activities, attributed costs and were managed. The role of performance management regimes was central to creating these markets in specifying what should be done and how it would be delivered, priced and measured (Bovaird and Gregory, 1996). In many cases, there was a need for new market making such as in building control. The deregulation of building control was common across a number of Organisation for Economic Co-operation and Development (OECD) countries including in the EU, Australia and Canada (Meijer and Visscher, 2006; Van der Heijden, 2010).
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.022 | 0.033 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.110 | 0.020 |
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".