Trust in Science: Developing a Learning Environment to enable Public Understanding and Support for Evidence-based Information for Senior Secondary School Students and Students in Higher Education
Bibliographic record
Abstract
Drawing upon international, multidisciplinary expertise and the experience of participation in a pan-European hackathon, the authors describe the development and implementation of an online learning environment. Their hackathon project, named “Trust in Science”, recognised the importance of confidence in academic knowledge in the context of current societal transformations and constitutes an extension of education on the processes and principles of research integrity (RI). RI is described here as the quality of honest and verifiable methods and adherence to professional norms in research. The authors participated in the hackathon in response to the COVID-19 pandemic and the consequent suspension of classroom education. The emerging principles presented in this report may have more general application in current educational transitions.
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 | 0.044 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".