Seeing and feeling difficult history: A case study of how Canadian students make sense of photographs of Indian Residential Schools
Bibliographic record
Abstract
Students in social studies classrooms are faced with a barrage of images, many of which represent historical trauma and violence. Although photographs can be used as pedagogical tools to represent experiences of injustice and elicit deeper understanding, they also activate affective and unrelated responses in students. In this case study, I explore the responses of Canadian secondary students to a set of historical photographs found in textbooks and resources that focus on the Indian Residential Schools. Findings from the study indicate that student responses to images representing difficult knowledge are unpredictable. Students were affectively and emotionally provoked by the photographs to both accept and deny abuse, as well as make personal connections to their own experiences of schooling. These findings raise questions around the best ways to use photographs of historical injustice in classrooms, as well as the ethics of using photographs that represent the suffering of others.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.037 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".