Subjective aging: factors influencing individuals’ perspectives
Bibliographic record
Abstract
The subjective experiences of old age or any event or experience occurring in an individual's life can be researched through both qualitative and quantitative methodologies. Asking participants how they maintain self-presentation of themselves as they age provides useful insights, not generalisations, into the field of inquiry. Okun and Ayalon (2022) undertook an online survey in 2020 of 818 Israeli adults (342 women and 350 men) aged 65-90 to address this question. This article focuses on two main questions from the survey: 'How do you define yourself?' and choosing and ranking by participants of the old age terms they preferred from a list that included the eight most common terms in the Hebrew language. The majority of the sample were aged 65-75 years, were married, were academic (education for more than 12 years), were retired and had no economic problems. They found that participants employed three strategies for self-presentation: absence of old age, camouflaged aging or multiplicity of old age terms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 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".