Bibliographic record
Abstract
Over the last months, the Massive Open Online Course (MOOC) debate has finally come of age, especially after Sebastian Thrun publicly announced that "we have a lousy product" (Chafkin, 2013), and a series of backlashes led to the conclusion that MOOCs mostly benefit those learners with a lot of cultural capital.Before this turning point, MOOCs were portrayed as a completely new educational innovation, and its conceptual ancestors such as distance education were ignored.Furthermore, other types of MOOCS such as the those based on the notion of connectivism, advocated by scholars such as Stephen Downes, George Siemens, and Rita Kop as well as the work around open content (Wiley & Gurrell, 2009), have been squeezed out of the collective memory.However, these approaches are located within a certain culture that frames our thinking and acting about pedagogy.More precisely, the open education paradigm (for an overview, Deimann & Sloep, 2013) has been dominated by Anglo-American actors such as the Massachusetts Institute of Technology which started the global Open Educational Resource (OER) movement a decade ago by opening up their teaching materials to
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.116 | 0.094 |
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".