Bibliographic record
Abstract
Click to increase image sizeClick to decrease image size Notes 1. For example, seeAdams, Bell, and Griffin (1997, 2007) Adams, M., Bell, L. A. and Griffin, P., eds. 1997. Teaching for diversity and social justice: A sourcebook, New York: Routledge. [Google Scholar]; Ayers, Hunt, and Quinn (1998) Ayers, W., Hunt, J. A. and Quinn, T., eds. 1998. Teaching for social justice: A democracy and education reader, New York: New Press and Teachers College Press. [Google Scholar]; Barry (2005) Barry, B. 2005. Why social justice matters, Malden, MA: Polity. [Google Scholar]; Davidson and Schniedewind (1997) Schniedewind, N. and Davidson, E. 1997. Open minds to equality: A sourcebook of learning activities to affirm diversity and promote equity, , 2nd ed., Boston, MA: Allyn & Bacon. [Google Scholar]; Enns and Sinacore (2005) Enns, C. Z. and Sinacore, A. L. 2005. Teaching and social justice: Integrating multicultural and feminist theories in the classroom, Washington, DC: American Psychological Association. [Crossref] , [Google Scholar]; Marshall and Oliva (2006) Marshall, C. and Oliva, M. 2006. Leadership for social justice: Making revolutions in education, Boston, MA: Pearson. [Google Scholar].
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 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 | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".