Bibliographic record
Abstract
The law has historically granted damages for some forms of non-pecuniary losses. In doing so, courts have freely admitted that there is imprecision in quantifying such losses and that there is no quantitative and objective calculus on pain and suffering. Against this background, new research on how hedonic losses are experienced by a victim provide an opportunity to review how non-pecuniary losses should be compensated. Some of this research suggests that experiences of anxiety, frustration and suffering may not affect a victim’s happiness as great as is presupposed in current models of compensation, and further, that its impact may also be ameliorated by the offering of an apology. In this essay, the author asks whether the law can incentivize tortfeasors to offer an apology as an element in mitigating compensatory damages for non-pecuniary loss Históricamente, el derecho ha concedido daños y perjuicios para algunas pérdidas no monetarias. Al hacer esto, los tribunales han admitido que existe una imprecisión a la hora de cuantificar estas pérdidas y que no existe un cálculo cuantitativo y objetivo del dolor y el sufrimiento. En este contexto, nuevas investigaciones sobre la experiencia de las víctimas frente a pérdidas hedonísticas ofrecen la oportunidad de revisar cómo se deberían compensar las pérdidas no pecuniarias. Algunas de estas investigaciones sugieren que las experiencias de ansiedad, frustración y sufrimiento pueden no afectar a la felicidad de una víctima tanto como se presupone en los modelos actuales de compensación, y además, su impacto puede mejorar al ofrecer una disculpa. En este ensayo, el autor se pregunta si el derecho puede incentivar a los causantes del daño a ofrecer una disculpa como elemento con el que reducir la indemnización por una pérdida no pecuniaria. DOWNLOAD THIS PAPER FROM SSRN: https://ssrn.com/abstract=3029460
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".