Bibliographic record
Abstract
Within the last twenty years, many states have been using quasi-market principles such as those expounded by New Public Management and the Structural Adjustment Programme (SAP) to reshape their administration. This is often perceived as a ‘one-size- fits-all approach’ to administrative reforms. This dissertation utilises contingency theory to evaluate the implementation of administrative reform policies by comparing Ghana and Ontario in order to analyze whether the ‘one-size-fits-all’ approach to administrative reforms is, in fact, the case. In particular, the dissertation examines privatisation and performance management systems as policy options for changing the administrative state. The study shows that countries face different institutional and capacity constraints. In addition (a) their histories; (b) levels of socio-economic and political development; (c) their governance systems; (d) the extent of external influence; and (e) their culture play a key role in the success of policies developed to change the administrative state. It suggests that in order to tailor the reforms to a country’s environment, these variables must be taken into consideration when administrative reforms are being planned. In conclusion, the dissertation confirms the argument that due to environmental differences ‘one-size-does-not fit all.’ It shows that policies that have worked in a particular country will not necessarily work in another, especially when the countries in question of transfer are developed and developing ones with markedly different cultural heritages.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".