Bibliographic record
Abstract
in Kuala Lumpur, Malaysia.It is a tremendous honor for the Cambridge Innovation Center (Singapore) to serve as the collaborative institution to enhance collaboration and mutually develop for being an international platform.ICLRC 2024 focuses on high quality presentations and papers that address contemporary issues on fundamental research leading to new methods, or adaptation of existing methods for new applications related to the topics of language studies and cultural communication.It aims to deliver an outstanding global forum for academics, researchers, scientists, engineers, students in the world to link up, exchange information and discussion.The conference has 6 keynote speeches and 9 invited speeches in total, and it has drawn about 120 delegates from 9 countries (China, India, Canada, United Kingdom, United States, Singapore, Malaysia, Australia, Philippines).The conference comprised a diverse spectrum of highly technical presentations by keynote and invited speaker sessions and authors of submitted papers.We are pleased to present the SHS Web of Conferences of the ICLRC 2024 and we sincerely hope that all participants and interested readers would be benefited from this proceedings.We want to express our heartfelt appreciation to our contributors, sponsors, colleagues and associations that helped make this conference successful.We'd also want to thank everyone on the committees and editorial board for the assistance and feedback.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.327 | 0.209 |
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".