TITLE Bias in Textbooks Regarding the Aged, Labour Unions,
Bibliographic record
Abstract
The report opens with detailed summaries of historical background information for each of the groups involved and with a review of the literature on bias in textbooks in Canada, the United States, and other countries. Over a time span of six months, 211 readers evaluated 1,719 textbooks. Readers located 104 biases in 78 textbooks. The 23 biases against the aged occurred mainly in English primary texts. Bias by omission accounted for most of the 65 findings concerning labor unions; however, strong negative statements about unions constituted most of the other biases. The 16 biases against political minorities were mostly ones of omission. The investigltors believed that biases against labor unions could have a strong negative effect on student attitudes. Biases regarding the aged and political minorities, on the other hand, were not considered pronounced enough to negatively affect student attitudes. The
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".