Forgiveness. By Eve Garrard and David McNaughton. (Acumen Publishing Ltd, 2010. Pp. xi + 132. Price £9.99.)
Bibliographic record
Abstract
Eve Garrard and David McNaughton's book belongs to a series aimed at opening up philosophical questions to a wider reading public, and the goal of accessibility is very much evident in their text. The authors sketch the terrain of recent philosophical debates about forgiveness in clear, comprehensible terms. But, given the recent abundance of work on the topic, it is worth asking: does Forgiveness add anything of substance that is new, or merely provide a useful summary of previous debates? A reader interested only in the former should turn directly to Chapter Four. Others, however, will appreciate the rest of this short, readable book. Forgiveness has five chapters. Chapter One sets the pre‐philosophical stage, arguing that the positive reputation forgiveness possesses is partly attributable to post‐Christian culture, and partly to current therapeutic advocacy. This chapter also raises some important sources of unease, noting that paradigmatic cases of forgiveness seem unfair, and that such unfairness may be compounded in real life. These concerns motivate the view outlined next, as Garrard and McNaughton set out the moral case against forgiveness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.263 | 0.204 |
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".