“What’s Age Got to do With It”: an Examination Into the Developments Within the Field of Subjective Views on Aging
Bibliographic record
Abstract
Journal Article "What's Age Got to do With It": an Examination Into the Developments Within the Field of Subjective Views on Aging Get access Y. Palgi, A. Shrira, and M. Diehl. (Eds.) ( 2022). Subjective views of aging: Theory, research, and practice. Springer International Publishing. Samuel Van Vleet, MGS, Samuel Van Vleet, MGS Department of Sociology and Gerontology, Miami University, Oxford, Ohio, USA Address correspondence to: Samuel Van Vleet, MGS; E-mail: vanvlesc@miamioh.edu Search for other works by this author on: Oxford Academic PubMed Google Scholar Kate de Medeiros, PhD Kate de Medeiros, PhD Department of Sociology and Anthropology, Concordia University, Montreal, Quebec, Canada https://orcid.org/0000-0002-3995-3170 Search for other works by this author on: Oxford Academic PubMed Google Scholar The Gerontologist, Volume 64, Issue 1, January 2024, gnad140, https://doi.org/10.1093/geront/gnad140 Published: 27 October 2023 Article history Published: 27 October 2023 Corrected and typeset: 24 November 2023
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".