Missing methods: a call for holistic analysis of history textbooks
Bibliographic record
Abstract
The purpose of this article is to call for a holistic approach to the study of history textbooks. We engaged in an extensive analysis of textbook studies for the purpose of developing our own textbook study framework for the Thinking Historically for Canada’s Future project. We found that scholars rely on a narrow scope of research methods and that the field lacks attention to a holistic approach that broadens the researcher’s capacity to dissect positionality and perpetuation of historical knowledge(s). We demonstrate why it is beneficial for scholars to expand beyond narrow methods in the field of history textbook studies. Specifically, we illustrate that a holistic approach enables textbook researchers to further contest the often uncritical embrace of history textbooks and expand upon our understanding of the textbook as an artefact of our societies. In this article we significantly extend the yet to be taken-up call for a holistic approach to textbook studies.
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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 | 0.205 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.011 | 0.047 |
| Scholarly communication | 0.024 | 0.035 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".