Towards a Viable Response to COVID-19 from the Science Education Community
Bibliographic record
Abstract
The COVID-19 pandemic has touched almost every corner of the planet and continues to impact on lives, livelihoods, economies and cultures. It is both a human and a global phenomenon. Making sense of what is happening requires an understanding of a number of scientific ideas including viruses, transmission, incubation and vaccination. These are life and death issues and yet the public and their political leaders often display a deliberate mistrust of the science and scientists. How might the science education community respond? We pose a series of questions designed to provoke a strong response to COVID-19 from our community and our colleagues: “How well has the science curriculum prepared the world’s public for COVID-19?”; “How much science education should be online from now on?”; “Are we learning from the current situation?”; “Is science education research producing knowledge that protects society from catastrophic events?”; “How should our working practices change to make science education more resilient, more useful and more transparent?”; “What are the ethics and politics of social distancing and how do they affect science education?”; “What pedagogies might we need to turn to in the future?”; and, “What role should business and industry play in funding science education research and development?” In our attempt to stimulate the development of a vision for science education in the post-pandemic era, we offer initial thoughts about moving forward. What we offer is a departure point, an invitation for the community to engage with pressing issues in science education. The main question we pose is the following: What can be done, and what can be done differently? We envision that this paper will provide some guidance to the readers to re-think the complex systems and socio-political contexts within which people come to learn and practice science and to conceptualize these processes through a social justice lens. We argue that a social justice informed approach towards shaping a vision for science education in the post-pandemic era is of paramount importance and that failure to do so will only serve as a way of perpetuating existing inequalities.
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 | 0.056 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.033 | 0.024 |
| Scholarly communication | 0.031 | 0.032 |
| Open science | 0.004 | 0.048 |
| Research integrity | 0.052 | 0.065 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".