Bibliographic record
Abstract
In addition to addressing a number of topical and controversial SSI (health hazards associated with mobile phones, xenotransplantation, stem cell research, GM foods, and the like), the curriculum needs to turn the critical spotlight on science itself. In particular, encouraging students to direct careful and critical attention to the role and status of scientific knowledge, the procedures by which scientific knowledge is generated, validated and disseminated, the language in which it is communicated to other scientists, students and the wider public, the values that underpin the conduct of scientists, the moral-ethical issues raised by contemporary scientific developments, and the wider social, political and economic climate in which science is practised. If teachers are to present science and scientific practice in a critical light, they need reliable information about the kind of understanding their students are likely to have already. Methods for ascertaining those views, including questionnaires and surveys, interviews, small group discussions, writing tasks and classroom observations (particularly in the context of hands-on activities), have been extensively reviewed by Hodson (2008, 2009a) and will not be revisited here. While it is always dangerous to generalize from research findings, it is fair to say, as noted in chapter 2, that many students (and their teachers) hold confused, confusing, misleading or downright false views about science, scientists and scientific practice36, views that are compounded by similarly inadequate/unsatisfactory views located in science textbooks and curriculum materials, projected via the so-called “hidden curriculum”, encountered through informal learning experiences in museums, zoos and science centres, and promulgated by the popular media. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".