The potential dark side of remote work transformation: a social vulnerability and relational perspective
Bibliographic record
Abstract
The global COVID-19 pandemic led many organizations and employees to abruptly adopt remote work. This remote work transformation has broadly encompassed individuals from a diverse range of backgrounds (e.g. ethnicity, gender) and job roles. A multi-level, conceptual model is developed that explores its potential dark side, particularly for women and ethnic minorities. Drawing on social vulnerability theory and the relational framework of diversity, this manuscript explores how disproportionate exposure to remote work, surveillance and disciplinary mechanisms, along with adverse job and wage loss, job insecurity, and psychological health impacts for women and ethnic minorities may potentially permeate their workplace interactions and stimulate interpersonal conflict. Moreover, this model incorporates these relationships within their broader social context, shaped by the legislative framework and shared cultural beliefs such as social difference codes. Nevertheless, this model elucidates how certain organizational approaches to diversity management can potentially alleviate these disproportionate outcomes. Theoretical implications, future research directions, and recommendations for policy and practice are highlighted.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".