Location Matters: Everyday Gender Discrimination in Remote and On-site Work
Bibliographic record
Abstract
Remote work has dramatically transformed professional environments, sparking considerable scholarly interest in its impact on employees and organizations. Contributing to this burgeoning literature, we investigate how remote versus on-site work affects women's experiences of gender discrimination. Given that work location can alter the gendered nature of interactions, we focus on everyday gender discrimination: slights and offenses that occur in interactions and are perceived by recipients as reflecting gender bias. Integrating gender frame theory and scholarship on virtual work, we argue that the gender frame tends to be less salient in remote settings. Thus, we predict that women experience less everyday gender discrimination when working remotely than on-site. Moreover, because the gender frame is likely to be more salient during on-site work for younger women and those who work with mostly men, we expect that these women experience a particularly pronounced reduction in everyday gender discrimination when working remotely. To test these predictions, we developed a new measure of everyday gender discrimination and conducted an original survey of 1,091 professional women who work in the same job both remotely and on-site. We find that women consistently report less everyday gender discrimination in remote versus on-site work. This effect is particularly pronounced for younger women and those who interact mainly with men. Overall, this study advances research on how work location shapes workers' outcomes and experiences, enriches the literature on the trade-offs women face in virtual and on-site settings, and extends scholarship on the contextual factors shaping workplace discrimination. Funding: This work was supported by Social Sciences and Humanities Research Council of Canada [Grant 435-2020-0059], the Canada Research Chairs Program, and the Institute for Pandemics and the Institute for Gender and the Economy at the University of Toronto. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.16949 .The final published version can be found at https://doi.org/10.1287/orsc.2022.16949. Deposited by shareyourpaper.org and openaccessbutton.org. We've taken reasonable steps to ensure this content doesn't violate copyright. However, if you think it does you can request a takedown by emailing help@openaccessbutton.org.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".