Gauging Visibility: How Female Clerical Workers Manage Work-Related Distress
Bibliographic record
Abstract
Our aim was to explain how female clerical workers manage work-related distress, using a feminist grounded theory method. Thirty-seven interviews were conducted with 24 female clerical workers. They engage in the process of gauging visibility to manage a recognition-vulnerability paradox. To gauge visibility, they take the lay of the land by attending to threats, resources, and supports within withering or flourishing work conditions. When distressing events occur, they select tactics of taking it in, taking it on, or letting it go, which are influenced by the quality of their work conditions. Their efforts to manage distress affect their workplace visibility, potentially enhancing their recognition or exacerbating their vulnerability. Gauging visibility can either diminish or enhance employees' health and well-being. Our findings emphasize social processes and structural conditions, shift attention to organization-wide efforts to alter workplace conditions, and suggest initiatives that enhance employees' opportunities for recognition, safety, and collective actions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".