Bibliographic record
Abstract
This is the long-awaited third edition of the bestselling, multi-award–winning introduction to foundational concepts in social justice education. Accessible to students from high school through graduate school, this comprehensive resource addresses the most common stumbling blocks to understanding social justice . In response to the deep divides in public discourse, this edition provides a framework for reaching common ground on issues of justice within a pluralistic democracy. The authors have updated statistics, research, and examples, and revised discussion questions and extension activities to guide classroom dialogue and engagement with today’s complex issues. New topics include the science of sex and gender, and the political backlash against equity and racial justice efforts. The authors trace the roots of white supremacy globally in the history of colonialism. Concepts such as oligarchy, kleptocracy, and capitalism’s relationship to democracy are introduced and discussed. Is Everyone Really Equal? is an up-to-date and engaging textbook and professional development resource that includes many user-friendly features, examples, vignettes, and activities to not just define but illustrate key concepts. Book Features: User-friendly features such as accessible language, “definition boxes” to reinforce concepts, “perspective checks” to acknowledge multiple viewpoints, and “stop boxes” that anticipate and address common points of resistance. A glossary that includes an explanation of social justice language and its complexities and changes over time. Discussion questions, extension activities, and exercises for book groups and classroom study. An accessible resource for instructors in disciplines including education, sociology, political science, criminal justice, nursing, social work, health sciences, gender and sexuality studies, and race and ethnic studies in the United States and Canada.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.015 |
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".