Corrigendum to “Making Space for Reading as a Faculty Member” [Am J Pharm Educ 87 (2023) 100115]
Bibliographic record
Abstract
The authors regret that the order of authorship originally published is incorrect and should be listed as shown below. Kristin K. Jankea, Jordan R. Covveyb, Benjamin D. Aronsonc, Theresa L. Charroisd* aWulling Center for Innovation and Scholarship in Pharmacy Education, 7–125D Weaver-Densford Hall 308 Harvard St. SE, Minneapolis, MN 55455, United States bDuquesne University School of Pharmacy, 600 Forbes Avenue, Pittsburgh, PA 15282, United States cCollege of Pharmacy University of Findlay, 1000 N. Main St, Findlay, OH, 45840, United States dFaculty of Pharmacy and Pharmaceutical Sciences University of Alberta, 3–227 Edmonton Clinic Health Academy, Edmonton, AB, T6G1C9, Canada The authors would like to apologize for any inconvenience caused. Making Space for Reading as a Faculty MemberAmerican Journal of Pharmaceutical EducationVol. 87Issue 10PreviewIn academia, significant emphasis is given to the importance of writing. However, reading may be given less prominence in faculty work lives. The purpose of this commentary is to explore the educator’s relationship with academic reading, habits that support this practice, and points of reflection for creating space for reading in our professional lives. The authors engaged in a reflexivity exercise and discussion of our own motivations, emotions, and experiences associated with reading, and how these may change across our careers. Full-Text PDF
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.467 | 0.368 |
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".