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Record W4407107000 · doi:10.1075/jls.22008.kes

“That 3-word-ultimatum”

2025· article· en· W4407107000 on OpenAlexaff
Aris Keshav, Lal Zimman

Bibliographic record

VenueJournal of Language and Sexuality · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMarriage and Sexual Relationships
Canadian institutionsConcordia University
Fundersnot available
KeywordsWord (group theory)Ultimatum gameLinguisticsPsychologyComputer sciencePhilosophySocial psychology

Abstract

fetched live from OpenAlex

Abstract I love you is among the most meaningful utterances in English speakers’ organization of intimacy, yet remains understudied by scholars of language. This article investigates metapragmatic discourse about the first exchange of I love you between romantic partners using data from the social media platform Reddit. The discourse reveals a normative model in which I love you functions not only as an expression of emotion but a speech act that is ideologically constructed as committing the speaker and inviting the listener onto a relationship path that includes monogamy, long-term commitment, and eventually cohabitation and marriage. Normative discourses about and practices surrounding I love you thereby bolster the hypervalorization of romantic relationships, the validation of jealousy and possessiveness as expressions of love, and the naturalization of monogamy and normative Western family structures. Drawing on discourses of polyamory, asexuality, and disability/neurodivergence, we point to alternative possibilities for love’s meaning and expression.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.073
Threshold uncertainty score0.245

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0730.013

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 head0.045
GPT teacher head0.390
Teacher spread0.345 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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