Promoting and Protecting Apologetic Discourse through Law: A Global Survey and Critique of Apology Legislation
Bibliographic record
Abstract
The year 2016 was a milestone for the law-and-apology field, marking the thirtieth anniversary of the first general law aimed at enabling apologies for civil wrongs, introduced in Massachusetts in 1986, as well as the tenth anniversary of the Apology Act, enacted in British Columbia in 2006. The Apology Act seeks to promote apologies and apologetic discourse as an important form of out-of-court dispute resolution, chiefly by making apologetic statements inadmissible for proving liability in civil wrongs. It has served as a benchmark from which subsequent law reform efforts in Canada and abroad have been measured. In 2017, that benchmark was passed with the enactment in Hong Kong of the most ambitious apology law yet, which privileges not only statements of remorse, but also statements of facts embedded in apologies. This article summarises global apology legislation and court decisions to date. Part I considers each major jurisdiction, starting with the USA and concluding with Hong Kong. Part II draws some conclusions about where we have been and where we are going in our efforts to promote or protect apologetic discourse, including recommendations on interpreting existing laws and on drafting or redrafting apology legislation. El año 2016 supuso un hito en el campo del derecho y las disculpas, marcando el trigésimo aniversario de la primera ley general destinada a permitir las disculpas para daños civiles, aprobada en Massachusetts en 1986, así como el décimo aniversario de la Ley de Disculpa, aprobada en la Columbia Británica en 2006. La Ley de Disculpa busca promover las disculpas y el discurso de arrepentimiento como una forma importante para resolver disputas fuera de los tribunales, principalmente haciendo que las afirmaciones de arrepentimiento no fueran admisibles para probar la responsabilidad por daños civiles. Ha servido como ejemplo con el que comparar siguientes intentos de reforma jurídica en Canadá y el extranjero. En 2017 dejó de ser ejemplo a raíz de la promulgación en Hong Kong de una ley de disculpa más ambiciosa todavía, que da un trato de favor no sólo a las afirmaciones de arrepentimiento, sino también a las afirmaciones de hechos integradas en las disculpas. Este artículo resume la legislación general sobre disculpas y las decisiones judiciales hasta la fecha. La parte I considera cada jurisdicción principal, empezando por Estados Unidos y acabando por Hong Kong. La parte II plantea unas conclusiones sobre de dónde venimos y hacia dónde vamos en nuestros esfuerzos para promover o proteger el discurso del arrepentimiento, incluyendo recomendaciones sobre la interpretación de leyes existentes y en la redacción o reforma de la legislación sobre perdón. DOWNLOAD THIS PAPER FROM SSRN: https://ssrn.com/abstract=3028811
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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 | 0.020 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".