Bibliographic record
Abstract
This special edition of Literary and Linguistic Computing comprises a selection of the papers presented at the 17th Joint International Conference of the Association for Computers and the Humanities (ACH) and the Association for Literary and Linguistic Computing (ALLC), which took place at the University of Victoria, British Columbia (Canada), from 15–18 June 2005. The conference was a great success thanks to the efforts of the local organizing team headed by Peter Liddell and including Scott Gerrity, Stewart Arneil, Martin Holmes, Greg Newton, Judy Nazar and Ray Siemens. The academic programme was compiled by an international programme committee chaired by Alejandro Bia and comprising Julia Flanders, Neil Fraistat, Simon Horobin, Joseph Jones, Lisa Lena Opas-Hänninen, Concha Sanz-Miguel, Susan Schreibman and Michael Sperberg-McQueen. The joint conference of the ACH and the ALLC is the oldest established meeting of scholars working at the intersection of advanced information technologies and the humanities, annually attracting a distinguished international multidisciplinary community at the forefront of their fields.
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 | 0.001 | 0.006 |
| 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 | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.395 | 0.279 |
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".