Book Review: For the Love of Men: From Toxic Masculinity to a More Mindful Masculinity
Bibliographic record
Abstract
Toxic masculinity is both a term and a concept that has been used widely in recent years to discuss problematic behaviour, beliefs, and attitudes that many argue support the legitimation of the patriarchy.Conversations in media and public life surrounding toxic masculinity as a pattern of behaviour, or belief system, often miss the mark by not attempting to understand the roots of the problem with nuance, care, and empathy.Society takes a greater issue with men who exhibit toxic masculinity than with the systems and social scripts that create those men.Rather than simply acknowledging that toxic masculinity exists and calling it out as it presents itself in social spaces, author Liz Plank meticulously seeks to understand and discuss the roots and harm traditional masculinity creates in contemporary society, with care, compassion, understanding, and empathy.Her keen observations are approachable, deeply thoughtful, professionally researched, and funny.Plank's work explores and examines gender equality and inequality from a perspective that suggests that current frameworks are incomplete, as they do not adequately address the lives of men or masculine identity and acknowledges that boys and men also suffer under traditional views of masculinity.For the Love of Men is an important work that encourages readers to not only challenge their own assumptions and beliefs regarding masculinity, but to also examine the ways in which we unconsciously reinforce toxic Book Review: Mills xi
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.055 | 0.029 |
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".