Comparative International Perspectives on Education and Social Change in Developing Countries and Indigenous Peoples in Developed Countries
Bibliographic record
Abstract
Democratizing educational access and building capacity in developing countries and amongst indigenous peoples in developed countries may be elusive but are hopeful goals. Many developing countries are striving to reengineer their incoherent education systems at a time when they are most vulnerable, particularly with susceptibility to natural disasters, political unrests, and economic instabilities (UNESCO, 2007). Similarly, indigenous peoples in developed countries are seeking more control over education as they consider the long-term effects of educational policies that have been forced on them.Research on education and social change in developing countries has a long history (Glewwe, 2002; Hanushek, 1995; Sider, 2011). However, there is limited research on educational capacity-building in developing countries such as Kenya, Honduras, Haiti, Ghana, Hong Kong, India, Peru, China, and Thailand. Further, the educational frameworks by which Indigenous peoples (Maori, Canada’s First Nations, and American Indian/Alaska Natives) have been educated have some significant similarities to those encountered in developing countries. The compilation of chapters illuminates research and collaborative initiatives between the authors and local leaders in developing countries’ and Indigenous peoples in developed countries’ efforts to solve the complexity of social inequities through educational access and quality learning. The authors draw on theoretical lens, knowledge bases, and strategies, and identify trends and developments to provide the scope of educational improvement in a globalization context (Brooks & Normore, 2010; Jean-Marie, Normore & Brooks, 2009).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".