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Record W2111486966 · doi:10.1145/2145204.2145324

Impression management work

2012· article· en· W2111486966 on OpenAlexafffund
Alison Benjamin, Jeremy Birnholtz, Ronald M. Baecker, Diane Gromala, Andrea D Furlan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsSimon Fraser UniversityToronto Rehabilitation InstituteUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutonomyImpression managementChronic painSocial isolationPsychologyIsolation (microbiology)Context (archaeology)Social environmentWork (physics)Internet privacySocial psychologyComputer sciencePsychiatrySociologyEngineering

Abstract

fetched live from OpenAlex

Chronic pain is an illness that affects nearly a third of senior citizens. Uncontrolled chronic pain can manifest constantly and/or intermittently, and can disrupt seniors' ability to plan or to maintain synchronous and scheduled contact with others. Such disruptions can expose seniors to stigma from others who do not understand this illness, social isolation, and a range of challenges to their social autonomy. We present results from an interview study of 27 seniors with chronic pain exploring how they mitigate and manage these disruptions in their lives. Drawing on Goffman's theory of impression management, we found that participants invested significant effort into controlling both the context of interactions and others' expectations, in order to mitigate the potential negative social consequences of disruptions. In performing this work, seniors were selective about what information they revealed to others about their chronic pain and availability. Given such efforts, seniors with chronic pain have unique needs for technologies to support their social interactions.

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)0.012
metaresearch head score (Gemma)0.040
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 score0.081
Threshold uncertainty score0.273

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.040
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0090.003
Scholarly communication0.0080.003
Open science0.0030.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0810.016

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 head0.049
GPT teacher head0.420
Teacher spread0.371 · 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

Citations27
Published2012
Admission routes2
Has abstractyes

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