DIGITAL LABOR SOLIDARITIES, COLLECTIVE FORMATIONS, AND RELATIONAL INFRASTRUCTURES
Bibliographic record
Abstract
Research on digital worker experiences have shed light on the problematic realities of digital labor, which include increasing levels of stress and anxiety over financial and career instability, physical exhaustion, and isolation - all of which underscore the precarity that belie the optimistic facade of labor under the new economy. Given the multiple constraints underlying collective formation among digital workers, this panel explores the characteristics and dynamics of emerging forms of collective organisation among digital workers by reflecting on experiences from China, the Philippines, Brazil, and India. Examining experiences of digital labor organisation and challenges to build solidarity across national and even regional experience, the panel hopes to enrich the discussion in terms of the politics, cultural nuances, and local meanings useful for examining digital worker expressions of resistance and solidarity amidst continuing technological development and platform reforms. This close examination of diverse forms of collective organisation, as well as the relational infrastructures underlying them, aims demonstrate how workers challenge the dominant claims of global capitalism while steep in recognition of the opportunities that these offer.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".