Corrigendum to “Interrogating Our Views on the Impact of Education-Related Scholarship” [Am J Pharm Educ 7 (2023) 100085]
Bibliographic record
Abstract
In the first paragraph, the example framework, listed as “The Experiential Learning Management Tool” should read, “Educational Leadership Mapping Tool,” which is commonly referred to as “the ELM Tool,” as was developed at the University of British Columbia. Interrogating Our Views on the Impact of Education-Related ScholarshipAmerican Journal of Pharmaceutical EducationVol. 87Issue 6PreviewFrom junior faculty members to seasoned full professors, pharmacy educators have likely all felt pressure to focus on peer-reviewed publication. Although publication is an important part of an academician's work, have we missed something critical by not focusing greater attention on a more inclusive conceptualization of education-related scholarship’s impact? How can we describe the full impact of our education-related scholarship beyond traditional metrics (ie, publications, presentations, and grant funding) if the issue is not critically examined? With the growing expectations for scholarly teaching and interest in the Scholarship of Teaching and Learning in academic pharmacy in both the United States and Canada, this commentary examines and questions the current, often narrow, views on pharmacy educators’ scholarly impact. Full-Text PDF
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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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.026 | 0.021 |
| Insufficient payload (model declined to judge) | 0.079 | 0.053 |
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".