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Record W1980251620 · doi:10.3149/jmh.0901.3

The Language of Emasculation: Implications for Cancer Patients

2010· article· en· W1980251620 on OpenAlexaff
Mitchell A. Cushman, JoAnne L. Phillips, Richard J. Wassersug

Bibliographic record

VenueInternational Journal of Men s Health · 2010
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsDalhousie University
Fundersnot available
KeywordsEmasculationLinguisticsComputer sciencePsychologyBiologyPhilosophyEcology

Abstract

fetched live from OpenAlex

Language can be used both literally and metaphorically. In this article, we explore the metaphorical use of terms including impotence, castration and neutered, to better understand how these words are interpreted by both the public at large, and by the approximately half a million men in North America who, at one time or another, take chemically castrating drugs to control prostate cancer. Specifically, we examine contemporary, publicly accessible sources for keywords related to emasculation; i.e., the Internet, jokes, films and printed news reports. We find that these terms are almost always employed negatively. We conclude that the language of emasculation often faults the subject and implies general dysfunction and powerlessness—socially, politically, and sexually—adding to the shame and “othering” felt by cancer patients who are castrated out of medical necessity. In addition, we show that recent efforts to refer to sexual impotence more narrowly as erectile dysfunction fail to separate the metaphorical from the physical meaning of impotence, and do not solve the problem of the shame associated with medical castration. Society’s failure to recognize that castration is still common adds to the stigma of those who are emasculated for medical reasons.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.143

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)0.0000.000

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 head0.042
GPT teacher head0.464
Teacher spread0.423 · 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 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

Citations38
Published2010
Admission routes1
Has abstractyes

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