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Record W6964706365 · doi:10.25916/sut.26280811

Talking 'costs': Seniors, cell phones and the personal and political economy of telecommunications in Canada

2010· article· en· W6964706365 on OpenAlexaboutno aff

Bibliographic record

VenueSwinburne Research Bank (Swinburne University of Technology) · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsPhoneContext (archaeology)NegotiationGovernment (linguistics)Digital economyPoliticsPerspective (graphical)

Abstract

fetched live from OpenAlex

Seniors, defined as persons aged 65 and up, are becoming a much larger demographic in Canada at the same time as the wireless telecommunications industry is expanding. Yet in terms of academic research, few studies have examined how seniors understand and negotiate the influx of digital communications devices, such as the cellular telephone, into their world. Using industry and government data, as well as individual and group interviews and observations with over 120 Canadian seniors, this paper examines the repertoire of 'cost' in the group discussions held with this cohort. It does so from the perspective of a feminist political economy that takes into account individual experiences in the context of macro-level, structural analyses of institutions and industry. This preliminary study suggests that financial considerations play a significant role in seniors' cell phone practices and may lead to a strategic decision to impose restrictions on their use. These restrictions to access often run counter to the desire, amongst many seniors, to have access to a cell phone for 'emergency purposes'. The comments made by this cohort make apparent the way that personal economies within a household on restricted or fixed incomes intersect with the practices of the wireless industry and suggest future avenues for media and ageing studies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.225
Teacher spread0.214 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2010
Admission routes1
Has abstractyes

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