MétaCan
Menu
Back to cohort
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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.789
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.011

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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Explore more

Same topicDigital Mental Health InterventionsFrench-language works237,207