Disrupting and displacing methodologies in STEM education: Tinkering with theory towards eco-social justice
Bibliographic record
Abstract
The following presentation outlines how a special issue on disrupting STEM research methodologies was conceived, as well as some of the innovative research is being developed in the writing project (special issue). Since STEM fields are heavily funded, and through a particular relation to objectivity and industry wield the effects of power, attention to how they contribute, or do not contribute, to a ecologically and socially just futures is vital. The issue that will be outlined in this presentation specifically called for a disruption of STEM research as usual in terms of methodological and/or theoretico-methodological approaches. The authors/presenters discuss how they came to see the need for such a project, its stakes, and will then give an overview of the research being developed by an international group of dedicated STEM scholars.
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 | 0.081 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.016 | 0.150 |
| Scholarly communication | 0.040 | 0.036 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".