The Status of Arabic in Israel: Refiections on the Power of Law to Produce Social Change
Bibliographic record
Abstract
Abstract The status of Arabic in Israel gives rise to question. Israel is a rare case of an ethnic nation-state that grants the language of minority group with a legal status which is prima facie one of equality. Both Hebrew and Arabic are the official languages of the State of Israel. What are the reasons for this special state of affairs? The answer is threefold: historic, sociological and legal. In various ways the potential inherent in the legal status of Arabic has been depleted of content, and as a result of that, as well as other reasons, the socio-political status of Arabic closely resembles what you would expect the status of a language of a minority group in a state that identifies itself as the state of the majority group to be. This answer, however, is another source of puzzlement – how does such a dissonance between law and practice evolve, what perpetuates it for so long, is change possible, is it to be expected? We present an analysis of the legal status of Arabic in Israel and at the same time we proceed to try and answer the questions regarding the gap between the legal and the sociopolitical status of Arabic. We reach some of our answers through a comparison with the use of law to change the status of the French language in Canada. One of these answers is that given the present constellation in Israel, the sociopolitical status of Arabic cannot meaningfully be altered by legal means.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".