Book Review: Co-teaching and Co-research in Contexts of Inequality: Using Networked Learning to Connect Africa and the World, edited by Phindile Zifikile Shangase, Daniela Gachago, and Eunice Ndeto Ivala (Vernon Press, 2023)
Bibliographic record
Abstract
Introduc�onThis book seeks to address the paucity of literature about networked co-teaching and co-learning in the contexts of inequality, and specifically in the African contexts, including cross-continental collaborations.In doing so, it also considers the challenges of co-researching.The call for chapters was circulated just before the COVID-19 pandemic, and the chapters were written at the height of the pandemic.Three of the chapters explicitly consider the design of responses to the pandemic.The editors describe co-teaching and co-research as "teaching and research that connects educators and learners across different institutions and different contexts, be it across South Africa, Africa, or the world" (p. 1).One of the key insights shared by the editors and several of the authors is the critical importance of human connection and relationship building to successful co-teaching, co-learning, and co-researching.Another interesting feature of the book is the thread of digital storytelling which runs through many of the chapters.The authors present a rich selection of "compelling cases for engaging in co-teaching and/or coresearching to advance more socially just, supportive, and mutually favourable practices in HE, among local and international academics and their students as well as practitioners" (p.xxxv).The editors are based in three South African universities with very different histories, cultures, and resourcing, and the international group of over 40 authors comes from Australia, Brazil, Egypt, Kenya, New Zealand, South Africa, Uganda, the United States, and the United Kingdom.I have known of the excellent and pathbreaking work of many of the authors for several years through the e/merge online conferences and the e/merge Africa professional development network.I believe this provides me with useful knowledge of the context of many of the authors (as well as a small bias in their favour).The text will be of particular relevance to colleagues interested in teaching, learning, and research collaborations in contexts of inequality due to the "cornucopia of international, transcontinental, pan-African, inter-institutional, institutional, and university-industry cases" (p.xxxv) and theoretical frameworks used by the authors.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 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".