If the University Is the Computer, Where Does That Leave the Library? MOOCs Discovered
Bibliographic record
Abstract
Massive Open Online Courses (MOOCs) are disrupting the traditional view of learning and the academy. Using technology, high-quality courses taught by some of the brightest minds are now available to unprecedented numbers of students. The university now has the potential to be in the computer. If the university is truly in the computer, what does that mean for the library? In this plenary session, Meredith Schwartz from Library Journal shares highlights from her article “Massive Open Opportunity: Supporting MOOCs in Public and Academic Libraries,” with an emphasis on academic communities. Key topics include definitions, current and future trends, and the potential impact of MOOCs on the library’s role, financials, policies, and collections. From this paper, learn more about this growing phenomenon and how your library can be involved.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.028 | 0.040 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.031 | 0.012 |
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".