Corrigendum to: Exploring University Age-Friendliness Using Collaborative Citizen Science
Bibliographic record
Abstract
In “Exploring University Age-Friendliness Using Collaborative Citizen Science”, DOI: 10.1093/geront/gnaa026, the author list was incomplete. This has been corrected by adding Abby C. King, PhD’s name as a co-author. The acknowledgments have been updated: The authors would like to acknowledge the contributions of the citizen scientists involved in this project, the Age-friendly University Committee and working groups at the University of Manitoba, Dr. Richard Milgrom, as well as Ann Banchoff and the Our Voice team at Stanford University School of Medicine. Finally, the funding statement now reads: This work was partially supported by the University of Manitoba’s University Collaborative Research Program (Project Number 47155). This research was also funded in part by the Robert Wood Johnson Foundation Grant ID#7334 awarded to Dr. King. The original article has now been corrected.
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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 | 0.003 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.173 | 0.112 |
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".