Bibliographic record
Abstract
This is a presentation by John Willinsky, hosted jointly by UTS Library and the UTS Academic Board regarding open access and academic scholarship. \n \nWillinsky is currently on the faculty of the Stanford University School of Education. Until 2007 he was the Pacific Press Professor of Literacy and Technology and Distinguished University Scholar in the Department of Language and Literacy Education at the University of British Columbia (UBC). He is a Fellow of the Royal Society of Canada. \n \nWillinsky taught school in Ontario for 10 years and, with Vivian Forssman, developed the Information Technology Management program for high schools in British Columbia and Ontario. He is the author of Empire of Words: The Reign of the OED, Learning to Divide the World: Education at Empire's End, which won Outstanding Book Awards from the American Educational Research Association and History of Education Society, as well as the more recent titles, Technologies of Knowing, If Only We Knew: Increasing the Public Value of Social Science Research and The Access Principle: The Case for Open Access to Research and Scholarship -- the latter of which has won the 2006 Blackwell's Scholarship Award and the Computers and Composition Distinguished Book Award. \n \nHe retains a partial appointment at UBC where he directs the Public Knowledge Project, which is researching systems that hold promise for improving the scholarly and public quality of academic research.
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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 | 0.041 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".