Effective Punishment Through Forgiveness: Rediscovering Kierkegaard’s Knight of Faith in the Abraham Story
Bibliographic record
Abstract
Scheler (1973) proposed a model of punishment intended to re-establish a reconciled relationship between a harm doer and the person(s) harmed. Punishment was followed by genuine forgiveness, seeking genuine repentance from the harm doer, leading to the reconciliation of the relationship. This paper proposed that only a punisher having the character of a Knight of Faith (Kierkegaard 1985) could effectively implement this punishment process. The Abraham story provided an illustration of how a Knight of Faith (God) rehabilitated his relationship with Abraham using punishment and forgiveness. This process is, at an individual level, similar to one applied by Nelson Mandela in the South African Truth and Reconciliation Commission (Tutu 2000). It is argued that this process is more effective in achieving reconciliation and re-establishing effective relationships than traditional retributive approaches as typified by the Sarbanes-Oxley Act, enacted in response to the Enron and WorldCom scandals.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".