Bibliographic record
Abstract
In the face of globalization, states around the world have tried to regain legitimacy by using a policy framework known as new public management (NPM). In education, it promises to improve quality and competitiveness, by means of increasing control over educational institutions, implementing managerial techniques and submitting educational institutions to increased competitiveness. This article presents evidence from an exploratory study comparing senior college administrators' attitudes in the Canadian province of British Columbia and those of vocational school administrators in two German subjurisdictions toward NPM. Questions addressed include: (1) how NPM-related measures were perceived by those administrators; and (2) how differences in the way reform discourses are framed in the different subjurisdictions impact the administrators' attitudes towards NPM. Findings show that the level of agreement on different aspects of NPM was comparatively similar across countries and subjurisdictions. Correlational analysis, however, revealed differences between the administrators' semantic constructions of the reform policy. Interpretive analysis indicated that semantic constructions were linked to the varying policy discourses of the subjurisdictions. The article concludes that discourse is an important aspect when introducing NPM in educational institutions, since it lays the groundwork for the conceptualization of future policy discourses.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.018 |
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".