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Record W2770242893 · doi:10.1080/13611267.2017.1403545

How communication context impacts judgments of a potential peer mentor

2017· article· en· W2770242893 on OpenAlexaff
Emily Christofides, Eileen Wood, Amanda Catherine Benn, Serge Desmarais, Krista Westfall

Bibliographic record

VenueMentoring & Tutoring Partnership in Learning · 2017
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsWilfrid Laurier UniversityUniversity of Guelph
Fundersnot available
KeywordsClosenessInterpersonal communicationPsychologySelf-disclosureSocial psychologyContext (archaeology)PerceptionInternet privacyPsychosocialReading (process)Interpersonal relationshipComputer science

Abstract

fetched live from OpenAlex

Disclosure is a critical element of interpersonal relationships and individuals are often evaluated on what they share with others, whether in personal, professional, or learning contexts. Technology now allows for many different outlets for communicating with other people. We used experimental methods to explore the impact of communication medium (i.e. print diary, online diary, blog, or email) on psychosocial perceptions of a potential peer mentor. Female participants gave more positive mentor ratings on likeability, likeliness to disclose to the mentor, and perceived closeness than did males, but not on judgments of the mentor’s privacy. Participants judged the mentor to be more private when they viewed the print diary than in the online conditions and when reading the online diary than the blog (the least private condition). We also found that women were more likely to reciprocate disclosure when they viewed disclosures in the print condition than in the blog.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.118
GPT teacher head0.431
Teacher spread0.314 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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