Bibliographic record
Abstract
A twelve-year ethnographic study of the use of computers by older adults (70+) in a U.S. retirement community spans the years of 1996 to 2008. The challenges of learning about new information and communication technologies are chronicled through the personal stories of members of a self-started computer club. Described here are the social networks that resulted from computing as well as the emotional and physical costs of keeping up with cultural innovations throughout the life cycle. Also considered are the merits of a prolonged relationship with research participants.Двенадцатилетнее этнографическое изучение использования компьютеров пожилыми людьми (70+ лет) пенсионного сообщества США охватывает период с 1996 по 2008 гг. Испытания изучением новых информационных и коммуникационных технологий отмечаются через личные истории членов компьютерного клуба, созданного по собственной инициативе. Здесь описываются социальные сети, происходящие от использования компьютеров, а также эмоциональные и физические затраты, связанные с постоянно меняющимися культурными инновациями на протяжении всего жизненного цикла. Рассмотрены также достоинства длительных отношений с участниками исследования. Une étude ethnographique de douze ans de l'utilisation des ordinateurs par les personnes âgées (70+ ans) dans une communauté de retraite des États-Unis a été faite sur la période de 1996 à 2008. Les épreuves liées à l'apprentissage de nouvelles informations et des technologies de la communication sont enregistrées à travers les histoires personnelles des membres d'un club informatique auto-initié. Sont décrits ici les réseaux sociaux qui ont résulté de l’utilisation des ordinateurs ainsi que les coûts émotionnels et physiques liés à la maintenance d’un savoir des innovations culturelles tout au long du cycle de vie. Les mérites d'une relation prolongée avec les participants de la recherche sont également considérés.
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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 | 0.021 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.026 | 0.031 |
| Scholarly communication | 0.032 | 0.037 |
| Open science | 0.003 | 0.028 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".