Taming the Green-Eyed Monster: On the Need to Rethink Our Cultural Conception of Jealousy
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.079 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".