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Record W4387601212 · doi:10.1111/pere.12523

Desperate or desirable? Perceptions of individuals seeking dates online and offline

2023· article· en· W4387601212 on OpenAlexafffund
Trenton C. Johanis, Claire Midgley, Penelope Lockwood

Bibliographic record

VenuePersonal Relationships · 2023
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPopularityPerceptionPsychologyLuckOnline and offlineThe InternetInternet privacySocial mediaInternet usersSocial psychologyOnline participationApplied psychologyWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Abstract Past research suggests that people who use the Internet to pursue romantic relationships have been stereotyped negatively—as unattractive, desperate, or creepy. It is possible, however, that as finding dates online has grown in popularity, individuals who have themselves used online methods to meet a partner are less likely to apply these negative stereotypes than non‐users. In addition, as options for dating online have proliferated, it is not clear that users of all online formats are viewed negatively, or how perceptions of users of online methods might differ from perceptions of daters using various offline methods. This study examined perceptions of those who use various online (algorithm‐based, profile‐browsing, or social media) and offline (meeting through family/friends, luck, groups, work, or going out) methods to meet a partner. Participants ( N = 214), who were themselves users or non‐users of online methods of meeting partners, were recruited through Amazon's Mechanical Turk system to complete online questionnaires. Results indicated that participants viewed individuals who used online methods more negatively than those using offline methods; however, individuals who had themselves used online methods viewed other online users more positively than did non‐users.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.001

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 head0.186
GPT teacher head0.396
Teacher spread0.210 · 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.

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

Citations10
Published2023
Admission routes2
Has abstractyes

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