Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it