‘Be your own boss’? Normative concerns of algorithmic management in the gig economy: reclaiming agency at work through algorithmic counter-tactics
Bibliographic record
Abstract
The article explores the normative concerns raised for gig workers by algorithmic management (AM), by embracing an ethnographically sensitive approach to philosophical inquiry. Inspired by Michel de Certeau’s concept of ‘tactics’, the article suggests interpreting workers’ attempts to ‘trick the algorithm’ and escape some of AM’s constraints as ways to reclaim agency, in the absence of suitable organizational conditions for its affirmative exercise. The kind of agency specifically deployed by workers in cooperative settings is referred to as ‘contributive agency’, broadly defined as workers’ control over their contribution in multiple dimensions – epistemic, relational, participatory and protective. Contributive agentic capacities are not mere properties of agents, but organizationally mediated capacities that can be more or less enabled or constrained depending on the contributive context. It is argued that below a certain threshold, AM’s agency-constraining features are objectionable and desirable agency-enabling organizational conditions are identified in the four dimensions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".