Between Proactive and Reactive Coping: How Food Delivery Workers Cope With Algorithmic Management Threats
Bibliographic record
Abstract
This study explores how gig workers in the food delivery sector cope with algorithmic management threats. Algorithmic management involves using learning algorithms to manage and control workers in online labour platforms. Although it offers opportunities for platforms, algorithmic management may present threats for workers including anxiety, burnout, and isolation. While focusing on resistance in the context of algoactivism, limited attention has been paid to how workers perceive algorithmic management threats and the emotion-focused and problem-focused coping behaviours they employ to cope with them. Using Q-methodology, the research identified four types of workers displaying unique coping strategies: the Empowered Collectivist fosters resilience through collective meaning-making and emotional support; the Savvy Opportunist leverages technical literacy; the Isolated Denier struggles with opacity and isolation; and the Anxious Conspiracist is marked by over-adapting and conspiracy theorizing. Building on this typology, the study proposes a dynamic coping model in which proactive coping strategies can support a virtuous cycle of positive reappraisal, increased agency, and resilience.In contrast, reactive coping strategies may contribute to a vicious cycle of negative reappraisal, emotional exhaustion, and disengagement. The study refines coping theory in technology mediated work, contributing to a nuanced understanding of algoactivism and redefining worker agency under algorithmic management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".