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Record W4386471210 · doi:10.1097/pra.0000000000000738

Incels: An Introduction for Mental Health Clinicians

2023· review· en· W4386471210 on OpenAlexaff
Camille Tastenhoye, Juliette Dupré, Nina Ross, Tanya V. Bodnar, Susan Hatters Friedman

Bibliographic record

VenueJournal of Psychiatric Practice · 2023
Typereview
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMental healthPsychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

Involuntary celibates ("incels") refers to a group consisting mostly of heterosexual men that exists predominantly in online spaces. These men are united by a belief that they are victimized by a postfeminist society, leading to an inability to engage in sexual intimacy with women. Recent acts of mass violence have been linked to self-identified incels, leading to an increased need for awareness of incel worldviews, argot, and demographics among psychiatrists. Limited research exists to describe this heterogenous group, with existing data suggesting higher rates of depression, anxiety, and suicidal ideation within the demographic. This column reviews the incel worldview, existing literature, how to identify an individual with incel beliefs based on vocabulary and internet usage, and treatment modalities.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.063
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.006
Open science0.0020.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0630.041

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 head0.198
GPT teacher head0.548
Teacher spread0.349 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2023
Admission routes1
Has abstractyes

Explore more

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