Bibliographic record
Abstract
Abstract Textbooks are artifacts. They are made, used, interpreted, and understood in a wide range of ways. In this sense, regardless of its theoretical assumptions, textbook analysis is an evolving and pioneering task as textbooks bring about manifold knowledge, relationships, and emotions. When exploring the texts, images, and functions in and beyond the textbooks, researchers would recognize textbooks as interactive subjects in the social world rather than simply as content carriers. Although content analysis has long been employed as a methodology for textbook analysis, there are multiple pathways to investigate textbooks. The paper pays specific attention to interpretivist methodologies that may allow researchers to see the textbooks' interactive performance and impacts on others and researchers themselves. First, the paper reviews and pieces together previously established approaches and orientations of textbook studies. Second, the paper attempts to build a broad framework for analysing textbooks based mainly on Prior's and Cooren's arguments about reconceptualizing documents and texts, respectively. Third, the paper explores the implications of the analysis mentioned earlier and examines two interpretivist research methodologies, including symbolic interactionism and autoethnography, to open up the possibilities of rethinking textbooks as active social agents in human life instead of repositories of information and ideologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.046 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.059 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".