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Record W2760862211 · doi:10.1515/nor-2017-0396

Maintaining Connections

2017· article· en· W2760862211 on OpenAlexfundno aff
Mireia Fernández-Ardèvol, Kim Sawchuk, Line Grenier

Bibliographic record

VenueNordicom review/NORDICOM review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLandlineNewspaperSet (abstract data type)Media usePower (physics)PsychologySociologyComputer scienceInternet privacyAdvertisingMedia studiesSocial psychologyBusinessLinguisticsPhone

Abstract

fetched live from OpenAlex

Abstract The concepts of user and non-user are frequently deployed within media and communications literature. What do these terms mean if examined regarding age and ageing? In this article we explore and trouble these notions through an analysis of twenty-two conversations with a group of octogenarians and nonagenarians living in a retirement home. Their descriptions of their changing uses of media througout lifetime, and their encounters with mobile phones, computers, newspapers, television, radio and landline phones, are presented as a set of ‘techno-biographies’ that challenge binary divisions of use and non-use, linear notions of media adoption, and add texture to the idea of ‘the fourth age’ as a time of life bereft of decisional power. Speaking with octogenarians and nonagenarians provides insights into media desires, needs and uses, and opens up ‘non-use’ as a complex, variegated activity, rather than a state of complete inaction or disinterest.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0030.004
Scholarly communication0.0060.010
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0620.015

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.

Opus teacher head0.051
GPT teacher head0.384
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2017
Admission routes1
Has abstractyes

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