A Top Down Strategy to Enhance Information Technologies into Israeli Higher Education
Bibliographic record
Abstract
This article examines the integration of the new information technologies (IT) into Israeli higher education, and most particularly its research universities through a top-down strategy, initiated by the Israeli Council for Higher Education since the end of 1999. This top-down strategy has created a systemic change that will affect the many layers of university activities rather than in a random, sporadic manner undertaken by enthusiastic individuals. This article discusses the built-in contradictions and dilemmas in the process of adapting distance teaching methods by conventional universities in Israel (as well as in other higher education systems). It examines the merits of a top-down strategy aimed to implement the IT through a macro-level, systemic approach, and analyses the differential uses of the IT in Israeli higher education institutions, relating to variables of: access-outreach; teaching-learning processes; study materials production; data and information retrieval; administrative functions; the creation of "researcher" communities; inter-institutional collaboration; and associated costs. The article concludes with some suggestions for effective implementation of the IT in different types of higher education institutions in a comprehensive and systematic manner, that will take into account their academic ethos and organizational infrastructure, and cater to the unique needs and characteristics of their relevant constituencies.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".