Apology Accepted: How the Apology Act Reveals the Law's Deference to the Power of Apologetic Discourse
Bibliographic record
Abstract
“I’m sorry” is an incredibly versatile and powerful phrase. More than an expression of simple sorrow, these words of apology are a social action, and their impact can range from resolving an accidental bump between pedestrians, to healing a deep interpersonal rift, to reconciling a divided nation. It is this power this function of apology as a moral and social actor which justifies its protection from interference by another powerful social and moral actor: the law. British Columbia’s Apology Act1 safeguards apologetic discourse from the often corruptive force of law that can limit, commodify, or discourage apology. In so doing, the Apology Act reveals an instance of the law’s humility. By carving out a safe space for alternative methods of negotiating human disputes, we see the law’s implicit admission that there are instances in which apology has a superior ability to reinforce moral standards and reconcile damaged social relations. We see a moment of the law embracing an exception to the basic principles of evidence, in order to privilege the important social and moral work of apology over the law’s relentlessly logical quest for truth. !is paper will demonstrate the ways in which apology is often superior to the law in navigating the realms of the moral and social and how it must be protected from the powerful influence of the law in order to safeguard a discursive process that is vital to a civil society.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.059 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".