Bibliographic record
Abstract
Author Webpage Author Webpage This volume grew out of a conference on “Discourse Processing: Reference,” which was held in Vancouver, British Columbia, Canada, in February 2003 and was cosponsored by the Cognitive Science Program of Simon Fraser University and the Cognitive Systems Program of the University of British Columbia. The conference was the twelfth in a series of “Vancouver Studies in Cognitive Science” conferences. Jeanette Gundel, who was guest coordinator of the conference, chose the theme and invited the speakers. We owe a great debt of gratitude to Martin Hahn, of Simon Fraser University's Philosophy Department, for helping to organize the conference. Some of the speakers at that conference are not represented here (Jennifer Arnold, David Beaver, David Braun, Craig Chambers, and Ron Zacharski) since they did not submit their papers for inclusion in the volume. Andrew Kehler's paper was not presented at the conference, as he was unable to attend. The volume also includes papers from researchers who were not invited speakers at the conference, but whose contributions were invited for inclusion in the volume (Alan Garnham and H. Wind Cowles; Sungryong Koh, Tony Sanford, Charles Clifton Jr., and Eugene J. Dawdiak; and Massimo Poesio).
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.295 | 0.174 |
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".