Bibliographic record
Abstract
"The first comprehensive treatment of stereotypes and stereotyping, this volume synthesizes a vast body of social and cognitive research that has emerged over the past quarter century. Distinguished researcher David J. Schneider provides an unusually broad analysis of stereotypes as products both of individual cognitive activities and of social and cultural forces. While devoting careful attention to harmful aspects of stereotypes and strategies for countering them, the volume also examines the positive functions of generalizations in helping people navigate a complex world. Highly readable and up to date, the book's balanced coverage of major theories and findings makes it an indispensable sourcebook and text." "Among the book's unique features are four chapters addressing the content of stereotypes, an area that has been relatively ignored in contemporary treatments. Considered are the characteristics associated with over a dozen groups that have traditionally been the victims of stereotypes; why certain traits tend to be stereotyped more often than others; and how traits become attributed to particular groups." "Including many useful examples, and identifying important directions for future research, this book belongs on the desks of all researchers, professors, and students interested in stereotyping, prejudice, and discrimination. It serves as an outstanding text for advanced undergraduate- and graduate-level courses."--BOOK JACKET.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".