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Record W2807965848 · doi:10.1002/cb.1724

It is for you, or it is for me: How relationship dependence affects gift image consistency in romantic relationships

2018· article· en· W2807965848 on OpenAlexaff
Rifei Cong, Biao Luo, Tieshan Li, Chengyuan Wang

Bibliographic record

VenueJournal of Consumer Behaviour · 2018
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsConcordia University
FundersNational Natural Science Foundation of China
KeywordsConsistency (knowledge bases)RomancePsychologySelf-imageContext (archaeology)Gift givingSocial psychologyImage (mathematics)Computer scienceEconomicsPsychoanalysisArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Recently, consumer behavior research has been focusing on decision‐making in relationships. This research considers an important context, romantic gift giving, in which givers choose gifts that are perceptually consistent with both their own self‐image and the recipients' self‐image. Based on interdependence theory, we investigate how the relationship dependence between romantic couples can affect gift image consistency. According to the results, the giver's level of dependence plays a positive role in the consistency between the recipient's self‐image and the gift image (gift‐recipient consistency) and plays a negative role in the consistency between the giver's self‐image and the gift image (gift‐giver consistency). The mutuality of dependence could strengthen or weaken the effect of the giver's level of dependence on gift image consistency, and this effect is carried through the giver's relationship power.

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)0.003
metaresearch head score (Gemma)0.017
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 score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.093
GPT teacher head0.421
Teacher spread0.328 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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