Bibliographic record
Abstract
This paper is a response to an article, “Creepy White Gaze: Rethinking the Diorama as a Pedagogical Activity” (Sterzuk & Mulholland, 2011), published in the Alberta Journal of Educational Research, in which Sterzuk and Mulholland critiqued a heritage fair entry, “Great Plains Indians.” I report on a school-university collaborative research project that examined the ways in which out-of-school practices and knowledges of Canadian Aboriginal students might provide these students with access to school literacy practices. Grounded in a ‘funds of knowledge’ approach, this paper presents an alternative reading, explaining how students’ linguistic and cultural resources from home and community networks were utilized to reshape school literacy practices through their involvement in a heritage fair program.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.070 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".