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Record W4945039

SEX DIFFERENCES IN DATING ORIENTATIONS: SOME COMPARISONS AND RECENT OBSERVATIONS

2016· article· en· W4945039 on OpenAlexaboutno aff
Robert N. Whitehurst

Bibliographic record

VenueArchivos Argentinos de Pediatria · 2016
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyFeelingHuman sexualityDevelopmental psychologySocial psychologyDemographyGender studiesSociology
DOInot available

Abstract

fetched live from OpenAlex

This study of 679 social science students in an Ontario university attempts to up grade and retest some of the notions in the literature about male-female relations in dating. Specifically, the approaches of Walter, Lowrie, and Burgess and Wallin were tested in this sample using semantic-differential items. Dating experiences for these students were positive and reflected values and experiences consonant with current beliefs; females were close to males in scoring their dating as sexual and hedonistic, while the general pattern involved high ratings of dating as pleasant, educational, cooperative and growth oriented. Inexperienced daters saw their dating as more positive than experienced daters. For most in the sample, dating was an end in itself and was not status or competition exploitation oriented—at least for the most part, even though these elements have not notably disappeared. Males were seen as less often cooperative in dating than females, while men registered more feelings of exploitation. While few of the respondents rated their dating as sexually aloof (less than ten percent), men were seen as too sex-oriented and women as immature. Men less often perceive their dating experiences as growth oriented while women see men as more crude and less often genteel. The relatively heavy emphasis on sensuatity and sexuality by both males and females is seen as indicating some change in dating patterns.

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 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 score0.050
Threshold uncertainty score0.999

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.113
GPT teacher head0.350
Teacher spread0.237 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueArchivos Argentinos de PediatriaSame topicEvolutionary Psychology and Human BehaviorFrench-language works237,207