The 15th Conference of the European Science Education Research Association (ESERA) Proceedings Book Series-III
Bibliographic record
Abstract
It is my pleasure to introduce the proceedings of the 15th Conference of the European Science Education Research Association (ESERA) in Cappadocia, Türkiye.The conference was the first physical conference following the Covid-19 pandemic, and as such it was a wonderful opportunity for the ESERA community to reconnect in person and to learn about our respective research across Europe and beyond.I am particularly delighted that ESERA was able to provide a platform for early career researchers and new members who joined the ESERA family during this conference.Based on my interactions with the community at the conference, the event was intellectually stimulating and socially pleasant experience for all.The planning of the conference began when we were still in the midst of the pandemic, slowly emerging from a very challenging period globally.The three universities involved in the organisation of the conference -Hacettepe University, Gazi University, and Nevşehir Hacı Bektaş Veli University -provided enormous support throughout but it is important to note particularly the period following the devastating earthquakes in February 2023.On behalf of the ESERA Board and the ESERA community, I thank the leadership of the Local Organising Committee, namely Gultekin Cakmakci, Mehmet Fatih Tasar, and Mustafa Hilmi Colakoglu for the effective management of the preparations under such extraordinary circumstances.The theme of the conference was "Connecting Science Education with Cultural Heritage", one of the main goals of UNESCO, the United Nations Educational, Scientific and Cultural Organization.As a culturally diverse country with a rich heritage, Türkiye provided a brilliant context to consider research in science education in diverse social learning environments.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.259 | 0.177 |
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".