Bibliographic record
Abstract
The following publication contains book reviews of these titles:Ferster, B. (2016) Sage on the Screen: Education, Media and How We Learn. Baltimore: Johns Hopkins University Press. 197 pages. ISBN 13: 978-1-4214-2126-1.Littlejohn, A and Hood, N. (2018) Reconceptualising Learning in the Digital Age: The [Un] democratising Potential of MOOCs. Singapore: Springer. 108 pages. ISBN: 978-981-10-8892-6, ISBN: 978-981-10-8893-3.Hodgkinson-Williams, C and Arinto, P.B. (eds.) (2017) Adoption and Impact of OER in the Global South. Cape Town and Ottawa: International Development Research Centre & Research on Open Educational Resources. 606 pages. ISBN: 9781928331483.Rankin, J.G. (2016) How to Make Data Work: a Guide for Educational Leaders. New York: Routledge. 226 pages. ISBN: 978-1-317-35338-6.Saba, F. and Shearer, R.L. (2018) Transactional Distance and Adaptive Learning, Planning for the Future of Higher Education. London: Routledge. 212 pages. ISBN: 978-1-138-30233-4, ISBN: 9780203731819.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.479 | 0.501 |
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".