Preface: International Conference on Mathematics and Science Education (ICMScE 2021)
Bibliographic record
Abstract
Abstract ICMScE is one of the conferences held by Universitas Pendidikan Indonesia. This year, ICMScE was conducted on 12 June 2021 with the theme “Sustainable-Thinking Competences Awareness toward Society 5.0 in the Light of COVID-19”. This theme is considered to represent the needs of mathematics and science education in the present and beyond to meet a smart and sustainable society. Due to growing concerns about COVID-19, ICMScE 2021 cancelled its physical conference this year instead of shifting to a virtual conference. The ICMScE participants came from various universities in Indonesia and abroad; therefore, it became an opportunity for the participant to share knowledge, exchange ideas, and have the opportunity to collaborate. This conference presents five Keynote Speakers: Prof. Charles Hopkins from UNESCO Chair in reorienting teacher education towards sustainability, York University, Toronto, Canada; Dr. Ida Kaniawati, M.Si, from Universitas Pendidikan Indonesia; Prof. Benö Csapó from the University of Szeged, Szeged, Hungary; Prof. Muammer Çalik from Trabzon University, Trabzon, Turkey and Prof. Ts. Dr. Faaizah Binti Shahbodin from University Teknikal Malaysia. In addition to the keynote, there were also invited speakers from Indonesia who contributed to ICMScE: Prof. Dr. Nahadi, M.Pd., M.Si (Chemistry Education), Prof. Topik Hidayat, M.Si., Ph.D (Biology Education), Dr. Eko Hariyono, M.Pd (Science Education), Prof. Turmudi, M.Ed., M.Sc., Ph.D (Mathematics Education) and Prof. Dr. Wawan Setiawan, M.Kom (Computer Education). A total of 540 participants participated in ICMScE 2021, 451 of whom were presenters. After reviewing and selecting 144 selected articles to be published to the present proceeding List of Committee List is available in this pdf.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.281 | 0.129 |
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".