The Language of Emasculation: Implications for Cancer Patients
Bibliographic record
Abstract
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.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".