Gender and Age in the Professions: Intersectionality, Meta-work, and Social Change
Bibliographic record
Abstract
Sociologists have paid little attention to the shifting significance of gender to professional work. Nevertheless, there is evidence that the meanings attached to gender, and the gendering of work, have shifted over time, such that the experiences of newer cohorts of professionals differ from those of professionals in previous generations. In this paper, we show how combining intersectionality theory and life course approaches facilitates the exploration of inequalities by gender, class, and race/ethnicity across generations and age cohorts. We present empirical research findings to demonstrate how this approach illuminates the convergence of gender and age in the professions to confer privilege and produce disadvantage in professional workplaces. Subsequently, we introduce the concept of meta-work—hidden, invisible and laborious work performed by non-traditional and disadvantaged professionals—through which they endeavor to cope with structural inequalities embedded in the professions. As professions and professional workplaces are still designed primarily for middle-class, dominant-ethnicity men, professionals who do not fit these categories need to invest extra time and energy to develop individual strategies and tactics to cope with professional pressures in and around their work. Meta-work is intrinsically linked to the traditional and normative ideals surrounding professional roles and identities, and therefore is intimately connected with professionals’ sense of self and their feeling of belonging to professional communities. Meta-work, and the tactics and strategies that result from it, are important coping mechanisms for some professionals, enabling them to deal with rapidly changing work realities and a lack of collegial support. Finally, we highlight several areas for future research on the intersections of gender and age in the professions.
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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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".