Preferences for different representations of colonial history in a Canadian urban indigenous community
Bibliographic record
Abstract
When a social group’s history includes significant victimization by an outgroup, how might that group choose to represent its collective history, and for what reasons? Employing a social identity approach, we show how preferences for different representations of colonial history were guided by group interest in a sample of urban Indigenous participants. Three themes were identified after thematic analysis of interview and focus group transcripts from thirty-five participants who identified as Indigenous. First, participants expressed concern that painful, victimization-focused representations of colonial history would harm vulnerable ingroup members, and urged caution when representing colonial history in this way. Second, while colonial history was clearly painful and unpleasant for all participants, many nevertheless felt it was important that representations of colonial history tell the whole truth about how badly Indigenous people have been mistreated by outgroups. Participants suggested these brutal representations of colonial history could also serve the interests of their group by bolstering ingroup pride when representations also emphasized the resilience of Indigenous peoples. Finally, participants described how brutal representations of colonial history could help transform intergroup relations with non-Indigenous outgroups in positive ways by explaining present challenges in Indigenous communities as the result of intergenerational trauma. We discuss findings in terms of their relevance for ingroup agency and their implications for public representations of colonial history.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".