Calibrating the ‘right values’: the role of critical inquiry tasks in social studies textbooks
Bibliographic record
Abstract
The issue of how to represent a nation’s past in history textbooks has been the source of vigorous debate across a variety of educational contexts. Some textbooks have been criticized for their simplistic, nation-building stories and the meta-narrative of ‘progress’ they engender. While many contemporary textbooks include critical inquiry tasks for developing learners’ historical thinking skills, the extent to which they actually facilitate critical thinking is unclear. This article employs methods grounded in Systemic Functional Linguistics (SFL) for analyzing verbal and visual text to examine evaluative meaning in the core narrative and two critical inquiry tasks of a Canadian social studies textbook chapter. The findings show an uneasy coexistence between the aims of providing opportunities for critical engagement and communicating a cohesive story of the nation’s collective experiences. Rather than platforms for facilitating interpretive independence, the critical inquiry tasks appear to be spaces for drawing out or calibrating the ‘right values’ developed through the core narrative of the chapter.
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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 | 0.127 | 0.364 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.032 | 0.028 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".