Digital Gig Work Under Unstable Energy Infrastructures: Invisible Workarounds and Alternative Imaginaries
Bibliographic record
Abstract
Digital upskilling and remote work are frequently presented as pathways for displaced communities who face political, economic crises and employment barriers. The viability and stability of available energy infrastructures for these communities is critical to the long-term success of these proposed job trajectories. In this paper, we investigate how 17 Syrian refugees living in Lebanon navigate their local unstable energy infrastructures to conduct digital gig work and receive digital training. We found that digital gig work and training are workarounds to the political and social inaccessibility of local labor markets for refugees and that participants rely on a series of material energy strategies to optimize their electricity access. We argue that to support refugees conducting digital gig work and training, we must recognize and account for ecological, social, and technological limitations and frailties in the development of technological solutions and draw our attention to how infrastructures are perpetually undergoing processes of breakdown, repair, and renewal. We argue that this attention to ongoing transformation and renewal creates new opportunities for productive and creative reconfiguration as well as modes through which CSCW may intervene in unstable energy contexts. From this perspective, we emphasize the importance of better resourcing displaced communities with information regarding energy access and supporting them in establishing and strengthening their own visions for alternative energy systems and employment pathways.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".