Are Collective Punishment Policies Doomed to Backfire? A Social Identity Approach Analysis
Bibliographic record
Abstract
This paper examines collective punishment from the perspective of the social identity approach, demonstrating that targeting all members of a group tends to backfire by strengthening rather than weakening their shared social identity. The fundamental rationale behind collective punishment is to create pressure on innocent group members, expecting them to react internally against guilty individuals, thereby bringing about a behavioral change. However, three case analyses focusing on Western sanctions imposed on Russia, trade tariffs implemented by the Trump administration against Canada, and Israel’s systematic policies in the Palestinian territories indicate that this strategy generally fails to achieve its intended outcomes. In accordance with the social identity approach, such external threats generate a shared sense of fate and victimhood within the punished group, thereby reinforcing ingroup solidarity and the collective sense of “we”. Consequently, anger is directed not toward the perpetrators within the group but toward the external punisher, rendering the punishing actor’s objective of dividing the ingroup ineffective. The research concludes that collective punishment is a destructive instrument that deepens polarization, erodes trust, and ultimately proven ineffective, or even counterproductive, in achieving its goals. These findings strongly emphasize that punishment, beyond its ethical and legal dimensions, should be grounded in individual responsibility and applied exclusively to actual perpetrators to ensure fairness and effectiveness.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".