“It's not like [I'm] Chinese and Canadian. I am in between”: Ethnicity and Students' Conceptions of Historical Significance
Bibliographic record
Abstract
This article explores the relationship between students' ethnic identities and their ascriptions of historical significance to moments in Canada's past. Twenty-six grade 12 students living in an ethnically diverse urban centre in British Columbia, Canada participated. Phenomenographic research methods were followed, with a range of data informing the findings. In groups, students completed a “picture-selection task” during which they were asked to make decisions about the historical significance of particular events and themes in Canadian history. Students were asked to describe their ethnic identity and then reflect on the ways in which their ethnic identity may have influenced the decisions they made during the picture-selection task. Analysis determined that students employed five types of historical significance and three narrative templates to construct the history of Canada. Students used specific types of historical significance depending on the narrative(s) they used. The students' ethnic identities played a central role in determining which narrative template(s) they employed and the criteria they used to select the events for their narratives. Many students articulated complicated notions of their identities, with some perceiving that particular “sides” of their identity were at play, or in use, during the research task. Students were able to engage in metacognitive thinking because of a research design that pushed them to articulate their beliefs about the relationship between identity (self-ascribed) and the narrative they constructed. Implications for teaching and further research are explored.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".