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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".