Regulation of Academia in Israel: Legislation, Policy, and Market Forces
Bibliographic record
Abstract
The rapid development of Israel’s system of higher education in recent years has led to a sharp rise in the number of students, the establishment of new institutions certified to award degrees, and legislation and policy changes. The evolving circumstances are explored in the current article, which follows the sources, causes, and justifications for these changes. The study analyzes three major processes that occurred in Israel’s system of higher education since its reform in the early 1990s: the increase in the number of students, admission terms to the departments, and the demand for studies. The research findings indicate that it was the government’s decision to establish colleges in the early 1990s, rather than free market forces, that led to the considerable increase in enrollment for academic studies. Then again, free market forces appear to determine admission terms to the various departments in accordance with the principles of demand and supply. Furthermore, the government intervenes to regulate the supply of high-demand fields of study but does not complement this by acting to regulate demand trends, which are determined exclusively by the free will of applicants. Therefore, the research conclusion is that Israel has no clear well-formulated policy on higher education, a fact that allows the unrestrained detrimental domination of this system by free market forces.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".