A path for STEM outreach and sustainability education [STEM Education]
Bibliographic record
Abstract
Effective science, technology, engineering and math (STEM) education should encourage students to think holistically about the impact they will have on society beyond the classroom. It should encourage students to imagine, and problem solve for a better world in which we all strive to meet the United Nations Sustainable Development Goals (UN SDGs). STEM outreach should ensure that the makers and doers of STEM projects are empowered by mentors who want to have their own purpose surpassed by more innovative ideas of the next generation so that humanity is propelled forward – together. This article provides both conceptual and pragmatic examples of this vision for STEM outreach and sustainability and challenges the reader to start engaging these ideas towards the vision set out thirty-seven years ago called “Our Common Future.”
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".