Using the Web to Facilitate Active Learning: A Transpacific Seminar on Globalization and Law
Bibliographic record
Abstract
This afternoon, a student came to visit me in my office... “What I wanted to talk about,” he says, settling into the chair opposite me, “is the paper that’s due next Thursday, I am not exactly sure what you’re looking for.” ...Throughout his schooling, he has learned that the “right answer” is the one the teacher is “looking for.” It has been a precious insight, almost infallible. In the system of higher education I used to believe in, my perverse task would have been to detach Patrick from this reliable strategy and orient him toward another goal: finding out what he himself thinks. I would have spent the hour’s conference... trying to help him discover that he already has ideas of his own, and that these — not some regurgitation of his class notes — are the answer I’m looking for... As a convert to higher-education reform, I don’t go through all that nonsense anymore. I’ve downloaded a single answer on the website I’ve created for the course. I tell Patrick that all he has to do now is click the “Right Answer” icon, which will lead him to a concise summary of class discussion and lectures on the subject. I point out that if he needs more help he can always buy a term paper from Essay World or one of the other Internet companies that sells such products.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".