Where STEM binds, and ST(eee)EM flows: A case for the where in STEM discourse and practice
Bibliographic record
Abstract
STEM may encounter issues of educational relevance as it proliferates, generalizes, and becomes disconnected with its own climate. Most commonly, STEM is assessed by two criteria: economy and quality. This results in linear movement between these two. This work suggests that complex movement exists within the concept STEM that can be understood through STEM’s climate. By reconceiving STEM as STeeeEM, through the infusion of three additional e’s (environment, ecology, and ethics), movement infuses into the concept STEM, illustrating complex, non-linearity as integral to STEM, in what may be described as STEM weather. Over time, STEM weather may be observed as STEM climate. Usefulness, outlined as a) health/wellbeing, b) environmental/sustainable ethics and practices, and c) learning stickiness, is posited as a third STEM criterion, along with economy and quality. By developing of a concept of the whereof education, distinct where usefulnessis reflected upon in relation to STEM. Outdoor where is proposed as particularly useful to STEM. STeeeEMing STEM, by blending in a third criterion (useful), along with movement noticed through interactions of the three “e’s”, responds to STEM conceptual reification. In situating STEM learning experiences in relation to where’s climate, STEM presents revived educational relevance as a concept.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.069 |
| Scholarly communication | 0.023 | 0.028 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".