Union Makes Us Strong: Space, Technology, and On-Demand Ridesourcing Digital Labour Platforms
Bibliographic record
Abstract
The entry of on-demand ridesourcing digital labour platforms (OR-DLPs) in Kolkata, India, restructured the local taxi-cab service industry's economic geography and spatial practices. Notably, they eroded the significance of the spatial fixity of taxi stands operated by traditional trade unions, enmeshed in local society's partisan political dynamics. Therefore, OR-DLPs triggered a reconfiguration of the socio-spatial and political practices around the taxi-cab industry in the city. Globally, traditional trade unions have struggled to organise workers in informal work arrangements and DLPs. However, in Kolkata, the Kolkata Ola-Uber App-Cab Operator and Drivers Union has proved to be successful. They established hybrid and networked unionism through technological affordances, placing worker-organisers rather than external organisers at the centre of their organisational structure. Furthermore, they undertook tech-mediated resistance against the OR-DLPs, local bureaucracy (e.g. the police) and the state. We explore this context to examine the impact of OR-DLPs on labour geography, worker-organising and resistance practices, along with the revitalisation strategies of traditional trade unions in response. From a non-Western context, we expand the frame for CSCW and HCI scholars' ongoing efforts to design worker-centric technologies for resistance.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".