Bibliographic record
Abstract
This special issue of ReCALL is composed of 17 articles selected from presentations made at the WorldCALL 2003 conference, held May 7–10 2003 in Banff, Canada. Against all odds, during the heat of the war on terrorism, in the middle of the SARS crisis, approximately 250 people gathered in a breathtakingly beautiful town in the Rocky Mountains to discuss the latest advances in the field of Computer Assisted Language Learning (CALL). Registrants came to Banff for four spring days from fifty countries to take part in 158 lectures and poster sessions. The conference was steered by an international committee composed of members from twelve countries and organized by researchers from the Faculté Saint-Jean (Edmonton, Alberta), the University of Alberta (Edmonton, Alberta), and the University of Calgary (Calgary, Alberta). The programme committee was established at the University of Victoria (Victoria, British Columbia). The specificity of WorldCALL conferences is that they are truly international, taking place in various parts of the world and attracting specialists from all parts of the planet. One of the unique contributions of this conference is that participants from underserved regions of the world are particularly encouraged to share their experience in CALL. In this respect, the conference was very successful. This was made possible by awarding eleven scholarships to participants from selected countries. WorldCALL 2003 was particular in one respect: being held in Canada and organized by French and English speakers, the organizers decided to provide a bilingual environment where presentations could be made in either of Canada's official languages. This is reflected in the selected papers by the fact that some of the articles are in French.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.116 | 0.103 |
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".