Current Trends in Critical Discourse Analyses of Textbooks: A Look at Selected Literature
Bibliographic record
Abstract
Critical discourse analysis (CDA) has increasingly served to examine the content of textbooks. Given momentum by critical social inquiry pertaining to textbook content, this study looks at peer-reviewed literature drawn from three scholarly databases (JSTOR, ERIC, and SAGE; cross-referenced with searches on Google Scholar) that use critical discourse analysis for those investigations. Reviewing the selected literature, this study asks: What are the most represented approaches of CDA used for examining textbooks? What contextual themes appear to draw the most attention? In what fields of study are the examined textbooks situated? How do these emergent themes appear to be connected? What areas of research appear lacking in the collected literature? The findings illustrate that, while the methods of CDA and types of textbooks examined are diverse, the lion’s share of contextual attention and critical utility appear to be given to foundational approaches to CDA and textbooks used for English language teaching. Further research directions on textbooks from a CDA perspective are discussed.
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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 | 0.059 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.082 | 0.080 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".