Bibliographic record
Abstract
Today, STEM and/or STEAM frameworks dominate the discourse around science education and what constitutes a ‘scientific’ literacy. While no one definition prevails in the literature, this literacy is often defined in the context of a current national concerns and focuses largely on Eurocentric (western) models of science and/ or scientific knowledge in terms of concepts, models, theories, or principles. As it currently stands, the term STEM is mostly used when addressing educational policy and curriculum choices in schools, aimed at improving competitiveness in science and technology with implications for workforce and economic development (often with some missing voices from women and Indigenous communities). Without an important socio-cultural critique, education of this kind can maintain and promote hegemonic beliefs and values while ignoring collateral problems relating to scientific or technological development: many of which have been linked to social and environmental injustice. In this paper, I offer three perspectives in an effort to decentre the discourse around the STEM movement. Using the overlapping themes of biocultural diversity, two-eyed seeing and guided inquiry, I offer suggestions on how to reframe science education as an interdisciplinary practice centred on student and community needs. In these ways, science education can ‘get back to the real world’ and promote creative approaches to science literacy, problem solving and cultural inquiry.
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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".