Bibliographic record
Abstract
In an age of unprecedented corporate and political control over life inside of educational institutions, this book provides a needed intervention to investigate how the economic and political elite use traditional artifacts in K-16 schools to perpetuate their interests at the expense of minoritized social groups. The contributors provide a comprehensive examination of how textbooks, the most dominant cultural force in which corporations and political leaders impact the schooling curricula, shape students’ thoughts and behavior, perpetuate power in dominant groups, and trivialize social groups who are oppressed on the structural axes of race, class, gender, sexuality, and (dis)ability. Several contributors also generate critical insight in how power shapes the production of textbooks and evaluate whether textbooks still perpetuate dominant Western narratives that normalize and privilege patriotism, militarism, consumerism, White supremacy, heterosexism, rugged individualism, technology, and a positivistic conception of the world. Finally, the book highlights several textbooks that challenge readers to rethink their stereotypical views of the Other, to reflect upon the constitutive forces causing oppression in schools and in the wider society, and to reflect upon how to challenge corporate and political dominance over knowledge production.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".