TECHNOLOGY AND AGING FUTURES: DISCOVERING THE CONSUMER ELECTRONICS SHOW (CES) 2019
Bibliographic record
Abstract
Abstract The growing field of technology and aging or gerontechnology has largely been considered from a health perspective on technological intervention to ameliorate conditions of isolation, disconnection, inactivity, and loneliness, as well as provide efficient alert systems, transportation coordination, and emergency services. Contesting the image of a ‘digital divide’ separating younger from older generations, the recreational industry has also produced a seniors market of technological games, toys, apps, exercises, and social media. The four papers in this symposium, however, are individual critical reflections by a group of social scientists who visited the Consumer Electronics Show (CES) in January 2019 (Las Vegas) as part of an ethnographic project about the politics of the technical turn in gerontological studies. In particular, the authors gathered evidence from the CES to support their interests in four trends: a) The collecting, aggregating, and sharing of personal data by home surveillance, artificial intelligence monitoring, and self-tracking systems for commercial, insurance and work-place purposes, b) The popularization of healthy lifestyles based on technical and exclusionary models of ‘smart’, ‘fit’, and ‘optimal’ standards, c) The technical rhetoric that infuses designs for efficiency, speed, and convenience with anti-aging and ageist ideologies, d) The challenges to older people to manage their lives against the health risks, interventions, and expectations posed by technology-driven austerity programs. The papers have in common their creative interpretations of CES materials and shared concern about the many older groups whose insufficient access, skill, and resources will deny them participation in the technological imaginary of aging futures.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".