Public policy and social science training in Israel: The impact of structural change on the constitution of knowledge
Bibliographic record
Abstract
Recent reforms instituted In the network of higher education in Israel have focused on two elements: adjusting the managerial structure of the universities to make it more amenable to market criteria of efficiency and reducing the proportional weight of state funding to the universities compared to that allotted to the technical and professional colleges. The main elements of this process—increasing power of managers in academic institutions, shifting universities toward entrepreneurialism, the idea of the “service university,” and the “massification” of the system of higher education—are characteristic of similar changes in higher education in the U.K., the U.S.A., Canada, and Australia. This article examines the impact of organizational and structural changes on the categories of knowledge produced, and by extension on the production of knowledge itself. By examining changes in the organization of higher education in Israel and in particular in the social sciences, the article suggests that institutional and academic diversification have influenced the categorization of legitimate knowledge pertaining to society, the economy, and the political arena—the traditional terrain of the social sciences—and hence what is considered “knowledge worth knowing” about these subjects. Finally, the article points to certain political interests that have motivated this change, and examines their larger impact upon Israeli society.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".