Equity in Mathematics Education
Bibliographic record
Abstract
Following in the steps of the socio-political turn of the discipline, Equity in Mathematics Education: Addressing a Changing World emerged as a response of the editor and the chapter authors to the enormous changes that have in the last years occurred at a global level (for example, the ongoing war in Syria, the political [in]actions of powerful nations to fight climate change, the rise of far-right parties in many countries around the world, and so on). In recent years, massive migration waves from the Middle East have caused significant demographic changes to many European countries, Canada and the US, that are reflected in schools and classrooms. These observations have led this books contributors to reconsider the concept and/or practice of equity, and its related concept, social justice, and the role of mathematics education research in addressing and promoting a fairer world. Contrary to other, perhaps highly specialized books concerned with similar topics, this book aims to provide a smooth, yet deep introduction to those who are new to this research area.Equity in Mathematics Education: Addressing a Changing World contributes to the understanding of equity and its complex relations to mathematics education. It is anticipated that it will support individuals in teaching, educational research, policy making and planning, and teacher education, in becoming more aware of the interplay between school mathematics and socio-political issues that, ultimately, impacts the lives of learners and their communities, teachers as practitioners and as citizens, the wider society, and the world as a whole. Even though each chapter can be read independently of others, an engagement with all chapters in this volume will provide readers with a solid holistic understanding of the research territory of equity and mathematics education.
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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 | 0.001 | 0.003 |
| 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 | 0.008 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".