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Online Self-Disclosure

2019· book-chapter· en· W2955327786 on OpenAlex
Malinda Desjarlais

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in psychology, mental health, and behavioral studies (APMHBS) book series · 2019
Typebook-chapter
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMount Royal University
Fundersnot available
KeywordsSocial connectednessSelf-disclosureClosenessAnonymityPsychologySocial mediaCompensation (psychology)Internet privacyEmpirical researchRelation (database)Social psychologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Due to their audiovisual anonymity and asynchronicity, social media have the potential to enhance self-disclosure, and thereby facilitate closeness among existing friends. In this chapter, the author highlights findings relating to the beneficial social connectedness outcomes that can be linked to online self-disclosure, synthesizes relevant literature that addresses who reaps the most benefits from online self-disclosure, and makes suggestions to direct future research in this area. Theoretical perspectives are identified throughout the chapter that are relevant to understanding the benefits of online self-disclosure, the relation between personal characteristics as predictors of online self-disclosure, and moderating factors of the effect of online self-disclosure on social connectedness. Empirical findings support both social compensation and social enhancement perspectives.

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.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.885
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.001
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 head0.051
GPT teacher head0.450
Teacher spread0.399 · 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