Understanding gerontalentology from the lens of older adults’ participation in got talent auditions (2015–2020): a manifest content analysis
Bibliographic record
Abstract
Despite the physical limitations that characterize the older person population, the United Nations continues to rally for the recognition and appreciation of the talents among this group to boost global development goals. Evidently, the introduction and expansion of Got Talent® shows globally, with its inclusive format that allows older participants to showcase their talents are promising frontiers to facilitate the implementation of the 2030 Agenda for Sustainable Development and the Madrid International Plan of Action on Aging. This inquiry seeks to elucidate the trends in older person’s participation in Got Talent® competitions from 2015 to 2020. Twenty-six (n = 26) purposively selected Got Talent® audition videos involving older persons, uploaded on YouTube were subjected to Manifest Content Analysis (MCA). Results indicate a growing number of auditions involving older adults, with singing as the most prevalent talent being showcased, for the last 5 years. Theoretical and practical implications, limitations, and recommendations were also discussed.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".