Fostering Equity and Inclusion in Distributed Work: New Directions in Hybrid & Remote Work Research
Bibliographic record
Abstract
Distributed work arrangements - including hybrid and remote work - offer workers around the world choice in their work location, which should ostensibly promote equity and inclusion. However, numerous challenges may arise for equity and inclusion as organizations and their employees navigate the narratives, structures, and implications of distributed work choices. In this symposium, we seek to separate narrative from fact around the current trends in distributed work and to understand exactly how hybrid and remote work can be navigated to maintain (and ideally foster) equity and inclusion in organizations. The five papers in this symposium collectively explore the emergence of prevailing narratives around remote and hybrid work, challenge existing narratives that remote and hybrid work are a “double-edged sword” for women and minorities, and seek to understand how, when, and why hybrid work can be the “best of both worlds” for diverse teams. Our discussant, Dr. Pamela Hinds, a leading scholar in the study of distributed work, will close our symposium by synthesizing the presented papers and facilitating a discussion with the audience regarding the future directions for this important topic. Through this symposium, we aim to generate new insights about how scholars can continue to study and improve the research on equity and inclusion within distributed work arrangements. How Journalists Amplified a Work Disengagement Narrative Justifying Remote Work Retrenchment Author: Leroy Gonsalves; Boston U. Questrom School of Business Author: Charles Chu; Boston U. Questrom School of Business Remote Work and Employee Performance and Promotability: Is There A Gender Gap? Author: Sumita Raghuram; San Jose State U. Author: N. Sharon Hill; George Washington U. Offsite At Office: How Temporary Colocation Shapes Communication in a Fully Remote Organization Author: Victora Sevcenko; INSEAD Author: Charles Ayoubi; Harvard Business School Author: Sujin Jang; INSEAD Author: Prithwiraj Choudhury; Harvard U. How Dimensions of Hybrid Teamwork Influence Team Empowerment Author: Thao Phan Hanh Nguyen; Cornell U. Author: Bradford S. Bell; Cornell U. The ABCs of Successful Hybrid Work Teams: Affective, Behavioral, and Cognitive Concordance Author: Devin Kilpatrick; U. of Michigan, Ross School of Business Author: Lindred L. Greer; U. of Michigan, Ross School of Business Author: Sherry M. B. Thatcher; U. of Tennessee, Knoxville
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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 | 0.046 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.081 |
| Scholarly communication | 0.034 | 0.058 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".