Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this small scale study in higher education, a good educational practice on the teaching of Bioethics based ontransformative learning and accomplished by debates is presented. The research was carried out in June 2016 at theDepartment of Molecular Biology and Genetics, Democritus University of Thrace, Greece and it includes theassessment of the debating experience by the students participating in the course. The research followed thequalitative method and data was collected by free association through a single question posed to students, askingthem to critically reflect on their debating experience. Content analysis was used for analyzing their responses.Debates seem to be a good practice for teaching Bioethics, since it leads to transformative learning for the futurescientists, as it is highlighted by the students’ views. They strongly state that they were highly interested andmotivated by their participation in debates, an active teaching method that promotes the development of criticalthinking, questioning, processing and presentation of scientific data, as well as the improvement of communicationand cooperation skills. The most significant finding, though, was the critical reflection that young students reachedregarding the subtle, difficult ethical issues of Biosciences.
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.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it