Biotechnology literacy: Assessing the knowledge and attitudes of student teachers
Bibliographic record
Abstract
In modern society, science and technology have become key fields affecting the daily lives of all citizens. Furthermore, young people will need this new knowledge in their future careers and in their daily lives as members of a technologically-imbued society. Developing a level of biotechnology literacy across Canadian society may become an essential factor in future prosperity. Teachers need a high level of this literacy to foster students’ development of this competency.\nThis paper is based on preliminary results from an international survey in Canada, China, and Spain. The survey aims to better understand the knowledge and attitudes of student teachers towards biotechnology which could potentially reflect on their teaching in the future; in other words their level of biotechnology literacy. The validated survey was administered to intermediate/senior and primary/junior pre-service students in the Faculty of Education. Canadian survey results for the primary/junior pre-service students are compared with those in the intermediate/senior program. These results will be compared to those obtained in other countries.\nSince biotechnology is having an ever increasing impact on society, this research will provide insight into the extent pre-service education programs need to be expanded and re-developed in order to incorporate and address the growing need for such knowledge.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".