Canada’s history with Indigenous peoples: do reminders of ingroup wrongs and ingroup identification influence collective guilt, moral shame, and reparation intentions?
Bibliographic record
Abstract
Research suggests historical accounts of past harm committed by an ingroup toward an outgroup \nhave elicited emotions such as collective guilt and moral shame. The present experiment \nexamined whether an explicit account regarding abuse committed against Indigenous peoples in \nthe residential school system elicited collective guilt, moral shame, and reparation endorsement. \nIngroup identification was assessed as a potential moderator of these predicted effects. 108 nonIndigenous students from Laurentian University were randomly assigned to excerpts derived \nfrom high school history textbooks were explicit or evasive and completed self-report \nquestionnaires. Results showed ingroup identification was a significant moderator whereby high \ningroup identifiers demonstrated greater levels of guilt and monetary support for Indigenous \nCanadians when exposed to the explicit text. Low ingroup identifiers had greater shame when \nexposed to an evasive text. Therefore, ingroup identification had an important influence on the \nmoral emotions and reparation intentions following exposure to an explicit vs. evasive account of \ntheir group’s past wrongdoing. Implications are discussed in relation to promoting a sense of \nresponsibility and intentions to repair ingroup wrongs, and how this might be facilitated in an \neducational context.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".