Bibliographic record
Abstract
In earlier articles, the author developed what is known as the "Supersession Thesis," asserting that historic injustice may be overtaken by changes in circumstances so that a situation that was unjust when it was brought about may coincide with what justice requires at a later time. The Supersession Thesis was developed initially as a tool for considering historic injustice suffered by indigenous peoples in the European settlement of countries like Australia, Canada, New Zealand, and the United States. In this paper, the author explores the application of the Supersession Thesis to issues about the Palestinian right of return and also to Israeli settlements in the Occupied Territories. The paper argues that, while it is not unthinkable that the Supersession Thesis might eventually legitimize the settlements and undermine the Palestinian right of return, there is no guarantee that this will happen. The application of the Supersession Thesis does not depend on the passage of time, but on changes in circumstances that a theory of justice makes relevant. Many of the circumstances that make the Supersession Thesis relevant to the post-colonial situations described (Australia, New Zealand, etc.) do not apply in the Israeli situation. Nevertheless, it is worth considering the possibility of applying the Supersession Thesis in this case, because it enables us to assess the merits of the Thesis more sharply in relation to injustice that is taking place now (or took place in living memory), as opposed to injustice that took place in the nineteenth century.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".