Beyond Tinkering and Tailoring: Re-de/signing Methodologies in STEM Education
Bibliographic record
Abstract
© 2018, Ontario Institute for Educational Studies (OISE). This commentary responds to the seven articles in this issue by reading them through lenses of tinkering and tailoring, juxtaposing and extending them with other writings across a range of fields. Disrupting and displacing methodologies in science education is not something new. There are multiple examples from two and more decades ago where science educators and researchers have drawn attention to the need to approach science education research and pedagogy differently. However, the authors in this Special Issue have worked from different theories in their efforts to go beyond tinkering and tailoring and re-de/sign methodologies in STEM education. We are inspired by the contributions and hope that these new approaches will achieve the changes that have eluded many similar arguments in the past.
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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 | 0.026 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".