Bibliographic record
Abstract
This book illuminates emerging perspectives and possibilities of the vibrant schooling and civic lives of Black African youth and communities in the United States, Canada, and globally. Chapters present key research on how to develop and enact teaching methodologies and research approaches that support Black African immigrant and refugee students. The contributors illuminate contours of the Framework for Educating African Immigrant Youth which focuses on four complementary approaches for teaching and learning: emboldening tellings of diaspora narratives; navigating pasts, presence, and futures of teaching and learning; enacting social civic literacies to extend complex identities; and affirming and extending cultural, heritage, and embodied knowledges, languages, and practices. The frameworks and practices will strengthen how educators address the interplay of identities presented by African, and by extension, Black immigrant populations. Disciplinary perspectives include literacy and language, social studies, civics, mathematics, and higher education; university and community partnerships; teacher education; global and comparative education, and after-school initiatives. Contributors: Susan Akello Ogwal, Sibel Akin-Sabuncu, Irteza Anwara Mohyuddi, OreOluwa Badaki, Joel Berends, Jasmine L. Blanks Jones, David Bwire, Nyimasata Damba Danjo, Liv T. Dávila, Priscila Dias Corrêa, Maryann J. Dreas-Shaikha, Patrick Keegan, Dinamic Kubangana, James Alan Oloo, Lakeya Omogun, Oyemolade Osibodu, Natacha Roberts.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".