The tables turned: Wilt Chamberlain versus Robert Nozick on rectification
Bibliographic record
Abstract
Recently the demand for rectification of past injustices has become an increasingly important issue. Each of the last three decades has witnessed democratization processes in the Mediterranean basin, Latin America, in Central and Eastern Europe and in Africa where debates have arisen over rectification of past wrongs which naturally include the unjust expropriation of property. Most recently, moreover, the idea of land restitution to indigenous people, particularly in Australia, Canada and Zimbabwe, has become a prominent, if not always equanimous, part of those countries' domestic political agendas. The difficulties associated with satisfying such demands have been discussed with particular regard to the framing of new post-Communist constitutions in eastern and central Europe by, among others, Jon Elster and Claus Offe. Yet, not only in the field of public policy has the issue of rectification become important. Perhaps because of these developments, there has been a resurgence of interest in rectification in political philosophy as is shown by a forthcoming collection of essays on the subject edited by Elster.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".