Bibliographic record
Abstract
This chapter looks at ways of examining informal e-learning environments to address innovative pedagogy, from two well-known institutions, where the theme of science is promoted within virtual centers, in a manner that is motivating for both online and onsite visitors. The author argues that real-time interactions such as Web casting act as a focus that enriches the people’s interest and thus enhances the notion of Public Understanding of Research (PUR), while “being socialized” through the scientific community. Science centers have recently expanded their mission beyond hands-on interactive exhibits, by adopting a reflective perspective drawn from a multidisciplinary approach to technological progress; that is, covering sociological, political, historical, philosophical and even ethical issues through online conferences and live demonstrations for visitors to become involved in topical debates. This allows them to form their own viewpoints on contemporary concerns ranging from genetic engineering and sustainability to space exploration. Within the diversity of educational resources offered by virtual science centers, it is suggested that museologists should emphasize a comprehensive description of scientific-related matters, tackling subjects, people and places, rather than objects themselves in order to genuinely fulfill a social need and arouse the audience’s curiosity.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".