A Difficult Journey: Transitioning from STEM to SoTL
Bibliographic record
Abstract
This essay unearths difficulties experienced by scholars trained in the STEM disciplines when transitioning into the research context that is SoTL. We, a scientist and an engineer, engaged in a series of audiotaped reflective discussions (facilitated by a social science researcher) designed to tease out the difficulties associated with this contextual shift. Our discussions pointed to issues that go beyond the oft-quoted methodological differences of a quantitative versus qualitative approach, speaking instead to barriers associated with: time, emotions, intellectual training and world-views. Embracing a complexity approach to the generation of knowledge and understanding led us to an appreciation of the role of narrative and allowed us to dissolve dualisms that we had associated with STEM and SoTL. Our next step is to extend the conversation to include other ‘scholar-travelers’ in a series of workshops aimed at addressing the barriers and bridges associated with journeying from STEM to SoTL.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".