Preface: 2023 International Conference on Researches in Educational Technology and Psychological Sciences (RETPS 2023)
Bibliographic record
Abstract
This is the proceedings for 2023 International Conference on Researches in Educational Technology and Psychological Sciences (RETPS 2023). This conference was held on October 07-08, 2023 in Houston, USA, and it was a very successful joint conference by four countries namely China, United States, Canada, and New Zealand. Papers from educational technology, public management, psychology, humanities, and social science were well represented in this volume and we hope that these contributions will continue to play a large part in future RETPS conferences.
 RETPS 2023 also fosters cooperation among organizations and researchers involved in the merging fields and provides in-depth technical presentations with ample opportunities for one-on-one discussions with the presenters.
 We wish to thank each and every member of the proceeding editors and local technical committee. Many people contributed to the success of the conference, and the organisers specially would like to thank the reviewer team. The local organisers and conference office did a splendid job, and we all enjoyed an excellent conference atmosphere. They were very patient and committed in making this conference a success.
 Organizing Committee of RETPS
 Houston, USA
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".