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Networks as Self-Defense: Identity and Compensatory Network Activation (WITHDRAWN)

2015· article· en· W2598560263 on OpenAlexaff
Edward B. Smith

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOpinion Dynamics and Social Influence
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsIntrapersonal communicationIdentity (music)PsychologySocial psychologyAffect (linguistics)Function (biology)Respite carePerceptionSocial identity theoryCognitionInterpersonal communicationSocial groupCommunication

Abstract

fetched live from OpenAlex

We argue that social networks function as more than pipes and prisms that transmit tangible and intangible resources interpersonally: they also serve people’s intrapersonal goals of identity maintenance. Three experiments considered how identity primes affected people’s representations of their networks. People who received feedback that disconfirmed their gender (Study 1) and political/ideological (Study 2) identities, subsequently recalled, or cognitively activated, networks that were smaller, denser, more emotionally supportive, more likely to be composed of people they have known for longer, and more likely to be composed of people associated with their disconfirmed identity. While identity-disconfirming information unsurprisingly triggered negative affect, people who then cognitively activated the types of networks mentioned above reported elevated moods. These results are indicative of an affirmational, compensatory function of (cognitive) social networks that allows people a psychological respite from situations that disaffirm self-perception. A final study (Study 3) investigated how this psychological process could affect more instrumental networking goals such as information search. By utilizing networks to serve identity-related goals, people may distort and block the network pipes that service effective network mobilization.

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 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: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.576

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.284
Teacher spread0.265 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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