Virtually Even: Status Equalizing in Distributed Organizations
Bibliographic record
Abstract
In distributed organizations, perceived status differences between workers are ubiquitous and harmful. Yet research suggests that once they are formed, status beliefs in organizations become entrenched in hierarchies and are hard to dismantle. In an inductive qualitative study, we observed how established status differences between remote and in-person workers in distributed organizations dissolved during the initial stages of the COVID-19 pandemic when everyone began working remotely. We use these data to theorize a novel status-equalizing process through which remote workers came to see themselves on an “equal playing field” with their in-person peers. We theorize how this status equalizing occurred through workers’ changing their “in-person default” use of technology—that is, their new behavior challenged embedded cultural practices that had treated the in-person workplace experience as the standard, normal, and valued perspective, implicitly guiding how employees used technology. Workers adopted new and more inclusive technology practices—including the use of asynchronous communication, greater codification of work, and virtual socializing—which resulted in remote workers perceiving new and more equal communication standards, access to information, and opportunity for social connection. As a result, these workers reported feeling less negatively stereotyped and treated more fairly in their virtual interactions with colleagues, fostering feelings of inclusion and deepening relationships across the previously established status divide. At a time when many organizations are grappling with the challenges of distributed, remote, and hybrid work, our research illuminates how inclusive technology practices can help nullify entrenched status imbalances.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".