Keeping the “Men” in Longshoremen: The Origins of Lasting Discrimination Against Women in the Longshore Occupation
Bibliographic record
Abstract
Abstract We study how one form of wrongdoing behavior – gender-based discrimination – evolved and steadily grew amongst longshoremen over seven decades (from 1947 to 2017), despite changes in the nature of work and technological innovations that made the occupation increasingly accessible to women. Using data collected from 72 interviews with retired and active longshoremen and their employers, supplemented with archival and observational data, we find that although women were permitted into the occupation at the beginning of the period (1947 to the 1960s), they were progressively, completely excluded by male longshore workers. We find that after experiencing imprinting (the idea that early experience exerts a crucial influence on later behavioral phenomena) (Immelmann, 1975) during early decades, longshoremen instrumentalized their fear of occupational decline and voluntarily engaged in organizational wrongdoing by discriminating against women. Men rationalized their exclusion of women through two means: first, by adapting the “Madonna vs temptress” paradigm of framing women, and second by strategically emphasizing self-serving justifications. This study contributes to the literature on gendered work and the difficulty of eliminating imprinted, entrenched behaviors in gendered occupations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".