Developing a Global Perspective in / FOR Science Teacher Education: The Case of Pollination
Bibliographic record
Abstract
Science educators at all levels continuously struggle to keep pace with the rapidly developing understanding of the causes and potential solutions to current environmental issues while also trying to enthuse a new generation of passionate and knowledgeable scientists. However, how can future science teachers make science education more attractive ad meaningful to their students? The present paper describes (in a narrative style) an instructional practice that has been performed within a secondary science methods course in a teacher preparation program in Canada. More specifically, it draws on ideas presented in Agenda 21 and the United Nation’s Millennium Development Goals to study the (often neglected) socio-environmental aspects of pollination. Ultimately, the proposed activity aims at promoting the ability of pre-service high school biology teachers to adopt a global education perspective on the science curriculum by (a) recognizing the unintended negative ecological impact caused by humanity’s pursuit of sustainable development and sustainability and (b) reexamining traditional conceptions of scientific and ecological literacies.
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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".