Beyond the inclusion/exclusion dichotomy: tensions between tasks, gendering practices and group outcomes
Bibliographic record
Abstract
Purpose This paper aims to explore how gendered task allocation contributes to workplace inequality, moving beyond the inclusion/exclusion dichotomy. Using a longitudinal study of science, technology, engineering and mathematics (STEM) professionals, it shows how gendered task distribution caused project delays and group failure. By challenging essentialist views, it highlights how patriarchal structures in task allocation perpetuate inequality in professional settings and stresses the need to address practices that sustain male dominance and impact outcomes. Design/methodology/approach Drawing on observation of interactions among members of a work group, this study investigates how tasks were allocated along gender lines. By looking at the status of tasks (“expert” or “menial”) we are able to see how a patriarchal regime is produced and reproduced in the group, reinforcing gendered power dynamics within the group. Unlike existing literature that suggests tasks are allocated based on efficiency, this research explores task distribution as a tool for maintaining patriarchal hierarchies. Findings Men resisted taking on “menial” tasks, which would place them in supportive roles, particularly in relation to women, leading to what we term “gender trouble.” This resistance was driven not just by gender stereotypes but, we suggest, by reflexive or unreflexive efforts to maintain patriarchal power structures. While this study focuses on STEM, these dynamics may also be present in other male-dominated fields, such as accounting. The study highlights how task allocation practices reinforce male superiority and impact overall group performance. Originality/value The paper’s originality lies in understanding gendered task allocation not as a question of “fit” between tasks and expertise or stereotypically masculine or feminine traits but instead as one that maintains male superiority consistent with underlying patriarchal structures. Our analysis extends our understanding of how gender dynamics shape work organization and power relations in male-dominated 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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 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".