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Record W2884460266 · doi:10.1145/3217804.3217907

Revisiting the Privacy Paradox

2018· article· en· W2884460266 on OpenAlexafffundabout
Anabel Quan‐Haase, Isioma Elueze

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInternet privacySocial mediaLimitingPersonally identifiable informationInformation privacyDigital literacyPsychologyComputer scienceWorld Wide WebComputer security

Abstract

fetched live from OpenAlex

Older adults (65+) use a wide range of digital media, yet have been slow in adopting social media specifically. In this paper, we explore to what extent privacy concerns could be a barrier to social media adoption in this demographic. By analyzing in-depth interviews with 40 older adult users and non-users of social media living in East York1, Toronto, Canada, we explore the types of social media privacy concerns that older adults have as well as the strategies they employ to mitigate these concerns. We found that older adult social media users and non-users shared similar privacy concerns; the most often mentioned being a concern for unauthorized access to personal information, and information misuse. While older adult non-users of social media protected themselves by avoiding social media, older adults who were social media users protected themselves by limiting the information they shared. This study has policy implications for training programs geared toward older adults and informs understandings of privacy literacy across the lifespan.

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)0.015
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.028
Scholarly communication0.0080.023
Open science0.0010.011
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0050.000

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 head0.044
GPT teacher head0.335
Teacher spread0.291 · 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 designTheoretical or conceptual
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

Citations41
Published2018
Admission routes3
Has abstractyes

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