Bibliographic record
Abstract
This timely and eye-opening book from Ke Zhang, Curt Bonk, Tom Reeves, and Tom Reynolds, MOOCs and Open Education in the Global South (Zhang, Bonk, Reeves, & Reynolds, 2020), provides 28 chapters that describe the challenges, successes, and opportunities of MOOCs and open education from the perspective of 68 authors from 47 countries in the Global South (http://moocsbook.com). Before those chapters, a detailed preface from the four editors lays out the journey that the world community took to get to this point in the metaphor of a wanderer who makes his or her path by pushing ahead and exploring the road in front. In addition, an insightful foreword is provided by Mimi Miyoung Lee from the University of Houston who had previously co-edited an award-winning book with Bonk, Reeves, and Reynolds; namely, MOOCs and Open Education Around the World (Bonk, Lee, Reeves, & Reynolds, 2015). Thus, consider the current book Part 2 of what is likely to become a many act play in the world of MOOCs and open education. With the foreword and preface, there are 30 pieces in total (Note: the front matter is available for free from: http://moocsbook.com/MOOCs_Open-Ed_Global-South-frontmatter_2020_Zhang_Bonk_Reeves_Reynolds.pdf).
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".