Bibliographic record
Abstract
International Advances in Education: Global Initiatives for Equity and Social Justice is an international research monograph series of scholarly works that primarily focus on empowering students (children, adolescents, and young adults) from diverse current circumstances and historic beliefs and traditions to become non-exploited/non-exploitive contributing members of the 21st century. The series draws on the research and innovative practices of investigators, academics, and community organizers around the globe that have contributed to the evidence base for developing sound educational policies, practices, and programs that optimize all students’ potential. Each volume includes multidisciplinary theory, research, and practices that provide an enriched understanding of the drivers of human potential via education to assist others in exploring, adapting, and replicating innovative strategies that enable ALL students to realize their full potential. Chapters in this volume are drawn from a wide range of countries including: Australia, Brazil, Canada, China, Finland, Georgia, Haiti, India, Italy, Kyrgyzstan, Portugal, Slovenia, Tanzania, Ukraine, and The United States all addressing issues of educational inequity, economic constraint, class bias and the links between education, poverty and social status. The individual chapters provide examples of theory, research, and practice that collectively present a lively, informative, cross-perspective, international conversation highlighting the significant gross economic and social injustices that abound in a wide variety of educational contexts around the world while spotlighting important, inspirational, and innovative remedies. Taken together, the chapters advance our understanding of best practices in the education of economically disadvantaged and socially marginalized populations while collectively rejecting institutional policies and traditional practices that reinforce the roots of economic and social discrimination. Chapter authors, utilize a range of methodologies including empirical research, historical reviews, case studies and personal reflections to demonstrate that poverty and class status are sociopolitical conditions, rather than individual identities. In addition, that education is an absolute human right and a powerful mechanism to promote individual, national, and international upward social and economic mobility, national stability and citizen wellbeing.
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 | 0.000 | 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) | 0.000 | 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".