Abstracts of the 3rd International Conference of TESOL & Education and VLTESOL2022
Bibliographic record
Abstract
The 3rd ICTE & VLTESOL2022 was hosted by the Faculty of Foreign Languages, Van Lang University, from 02 to 03 December 2022 at 45 Nguyen Khac Nhu, Dist. 1, Ho Chi Minh City, Vietnam. The Conference received more than 140 authors who submitted abstracts to the 3rd ICTE & VLTESOL2022. The authors come from 10 different countries, such as Indonesia (14), Russia (1), Malaysia (8), Canada (1), Nepal (1), Thailand (2), the United States (3), Japan (1), China (2), and Vietnam (107). The Conference was hybrid, in virtual mode for international delegates and offline for local & International representatives. The conference proceedings will be published in the ICTE Conference Proceedings (ISSN: 2834-0000), the International Journal of Language Instruction (ISSN: 2833-230X), and the International Journal of TESOL & Education (ISSN: 2768-4563)
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.320 | 0.108 |
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".