Indigenous Knowledge, Indigenous Experiences with Residential Schools and Sixties’ Scoop, and their Impact on Emotional Knowledge for Pre-service Teachers
Bibliographic record
Abstract
The purpose of this 2019-2020 exploratory study is to examine pre-service teachers’ knowledge and perceptions of Indigenous Peoples and how emotional knowledge could efficiently integrate this sensitive aspect of Canadian history into the B.Ed. Program. Shen et al. (2009) state that emotions improve learning and facilitate retention in long-term memory. Could emotional knowledge be a way of integrating Indigenous knowledge in the Bachelor of Education programs? Could Indigenous experiences with Indigenous Peoples make a difference in the perception of Indigenous Peoples in pre-service teachers? For this study, the sample available to the researcher consisted of 22 pre-service teacher students. The research instruments were a questionnaire about pre-service teachers’ knowledge of Blanket Exercises, Residential Schools, and Sixties’ Scoop, and reflection papers on the same topics. The results show that 72% of Canadian pre-service teachers, who attended elementary and secondary schools, had some, very little or no knowledge of these topics before the former Prime Minister, Stephen Harper, apologized to former students of Residential Schools for the harm inflicted to them. After listening to Indigenous Survivors and being part of Blanket Exercises, pre-service teachers’ perception of Indigenous Peoples changed in a range of 26% to 100%.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".