Review of: "How do older adults cope with their aging and age? A scale for an offensive coping strategy of older adults"
Bibliographic record
Abstract
Potential competing interests: No potential competing interests to declare. This is a very interesting paper, describing the need for and development of a test/tool to measure how older people manage their own aging, including what they think about their own aging. It is a very lengthy paper, and so readers need to ensure they have enough time to be able to read and understand why this test/tool is needed and how it was constructed and preliminary tested. It is nice to see this work, given population aging. It is very important for older people to not have internal ageism, where they are negative about their own aging bodies and minds. One major issue is the use of the term "offensive" -in English, this means nasty or unpleasant. It would be better if the word "PROACTIVE" or "POSITIVE" or "PREFERABLE" were used instead. I am not an expert on tool construction, but I believe this is a good step forward to help people feel more positively about their own aging.
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 | 0.011 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".