GENDER WAGE DISPARITY IN THE UNITED STATES: SOCIO-CULTURAL CONTEXT V. LEGISLATIONS
Bibliographic record
Abstract
This study aimed at investigating the contributing factors to the persistence of gender pay disparity in American workforce despite decades of the enactment of progressive, federal legislations concerning on women’s wage. This study employed sociological approach and utilized qualitative research to achieve its predetermined objectives. Utilizing library research, data were gathered and analyzed using gender theory, particularly the theory of devaluation of women’s work. The results of this study indicated that prevalent American cultural values on gender roles and pay secrecy interfere with the federal legislations concerning on women’s wage. Meaning to say, the socio-cultural context where the legislations are applied and enforced seems to be, in some ways, contradictory to the legislations. The data of this study showed that in the workplace, cultural values on gender roles affected the decisions in hiring and during the employment, which further resulted in gender discriminatory practices (in general) and gender wage discrimination (in specific). Meanwhile, the prevalence of cultural values of not talking about salary reinforced employer’s policy against salary disclosure (PSC rules), which led to the hindrance of wage transparency that is in fact, in contradictory to what the legislations suggested. As a conclusion, gender wage disparity could not be cured solely with the enactment of federal legislations. Evolutionary changes in cultural values of the society are also significant in eliminating the gender wage disparity in American workforce.Keywords: gender wage disparity, socio-cultural context, cultural values, federal legislations, pay secrecy
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".