Compassion, remote work and vulnerability: the case of employees with disabilities during the COVID-19 pandemic
Bibliographic record
Abstract
Purpose There is an increasing interdisciplinary interest in studying vulnerability in the workplace. Some scholars highlight the complex interplay of personal and situational factors that create vulnerable employees, while others, like us, view vulnerability as a universal condition with both positive and negative organizational implications. However, how organizations actively shape vulnerability remains unclear. Design/methodology/approach To this aim, we conducted 56 interviews with employees with disabilities who carried on working remotely during the COVID-19 pandemic. Findings New infrastructures – the social connections and structures that enable people to care for and rely on one another – fostered compassion and reduced barriers through remote work, creating hope of shared vulnerability. However, the persistence of ideals of a free and autonomous subject limited recognition of the unequal distribution of vulnerability, ultimately restricting solidarity. Originality/value This study introduces the idea of “states of vulnerability,” defined as moments when people in otherwise secure and safe employment are exposed to harm through organizational practices. It reflects on the practical requirements for vulnerability to emerge in organizations as sites of ethical engagement, fostering more sustainable careers.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".