Toward Collaborative Dialogue: Unpacking the Researcher–Educator Divide to Advance Chemistry Education
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Researchers, educators, and students have different roles in the chemistry education community and are subject to distinct evaluation criteria that inform how they approach their work. In this commentary, we leverage our experience as individuals positioned at the researcher–educator–student interface to describe how we consider these evaluation criteria to incentivize different priorities. These priorities often align synergistically but sometimes conflict. We argue that conflicts between priorities can lead to divisions among community members that undermine the achievement of our shared goals. Based on this understanding, we suggest examples of how the community might overcome these challenges by facilitating education research knowledge mobilization, expanding representation of chemistry education at the undergraduate level, and engaging in collaborative dialogue.
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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 | 0.114 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.072 |
| Scholarly communication | 0.030 | 0.026 |
| Open science | 0.006 | 0.028 |
| Research integrity | 0.026 | 0.041 |
| Insufficient payload (model declined to judge) | 0.003 | 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".