Bibliographic record
Abstract
This chapter reveals that human history is replete with examples of unjustified expropriations of property by conquering states and other transitory regimes. Only in modern times, however, have nations attempted systematically to remedy historical injustices by providing reparations to the dispossessed owners or their successors. From the aboriginal peoples of the Antipodes to the Native Americans of Canada and the United States to the European victims of the German and Soviet communism, groups of people who were stripped of their land and possessions by fraud or force are demanding, and in many cases getting, reparations for these injustices. The thesis of this chapter is that the case for reparations for such expropriations of property is highly tenuous, both morally and in practical terms. Reparations claims in general face two serious challenges: human irrationality and the effects of time. While these challenges are not necessarily insuperable, they are formidable.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".