Gender, income and managerial status among graduate social workers
Bibliographic record
Abstract
Two historical changes have impacted on women's relationship with work in the last century. The influx of young single women into the paid work force and the participation of married women in the paid work force have changed the world of work for men and women. Middle and upper class women gained access into the public sphere through the caring work of teaching, nursing and social work. Although the roles of women have expanded and in many ways changed, the struggle for equity is far from over. Women still earn, on average, significantly less than males in Canada. Female Social Workers earn significantly less than their male peers. Two opposing lenses are commonly used to explain the gender income gap, human capital theory and feminist theory. The former focuses on the role played by women's choices such as educational level or commitment to family. The later looks to the broader society and the ideology of patriarchy as the root for this disparity. This study explores a number of general hypotheses. Is there a gender income gap among Ontario Social Workers? If so what variables can explain that gap? Is there a perception of discrimination or oppression in this population? Is there a disparity in administrative status between males and females and if so what explains that disparity? A mailed survey to a sample of the alumni of two large Ontario social work programmes, one master level and one undergraduate, provided 1075 responses. Five multiple regression models are explored to determine predictors of income and administrative status. The results confirmed a significant income disparity between male and female social workers, favouring males by 20%. The gender income gap was even greater in administrative positions. Both gendered and gender neutral variables predicted this income gap. Some, like gender, point directly to discrimination. As well, the analysis revealed a significant difference in the proportion of males and females in administrative positions. Predictors of administrative status were less clear since females indicated adequate interest in administration and there appeared to be no discrimination in promotion practices. Finally, both frameworks were used to understand these results. Although both a human capital and feminist lens help to understand income and managerial differences between males and females in social work, a radical feminist perspective best explains the findings
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".