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Record W2280232413

Taming the Green-Eyed Monster: On the Need to Rethink Our Cultural Conception of Jealousy

2013· article· en· W2280232413 on OpenAlexaboutno aff
J. Randolph Tucker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicMarriage and Sexual Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsJealousyRomancePsychologySociologyCriminologySocial psychologyPsychoanalysis
DOInot available

Abstract

fetched live from OpenAlex

Western culture tends to view romantic jealousy as innate, and as an inevitable byproduct of romantic love. Relying on findings of empirical research, this Note argues that this view, widespread in America, is invidious. Likely as a result of this view, wrongful acts arising from jealousy are often excused or even condoned. This Note draws evidence from empirical studies on the contribution of jealousy to domestic violence, homicide, and divorce to show how this view can be detrimental to society. Additionally, this Note shows that the dual beliefs that jealousy is innate and inextricable from love are both incorrect. Evidence from cultural psychology and anthropological studies strongly suggests that the expression of jealousy is largely culturally determined. This Note also examines the polyamorist movement in the United States, Canada, and England as evidence both that romantic love can exist independently from jealousy and that jealousy may be controlled. Based on the sum of these findings, this Note goes on to consider societal acceptance of polyamory and polygamy as a potential step toward solving the problems posed by the dominant cultural view of jealousy. By helping to undermine our invidious cultural perceptions of jealousy, this Note argues that such an acceptance might reduce the incidence of jealousy-related problems. The Note concludes by suggesting measures that might be taken toward this end.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.345
Teacher spread0.258 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2013
Admission routes1
Has abstractyes

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