How Digital Platforms Materialize Sustainable Collaborative Consumption: A Brazilian and Canadian Bike-Sharing Case Study
Bibliographic record
Abstract
Pollution, resource depletion, and to a lesser extent, global warming called into question mass consumption. Public policies, media broadcasters, tech giants, and supranational entities (e.g., United Nations) nudged societies into alternative consumption forms that have been deemed more sustainable, such as collaborative consumption (CC). This paper aims at proposing a theoretical–empirical model that explains the materiality of sustainable collaborative practices through bike-sharing. The study further analyzes how connections, mediations, and inductions occur between individuals, platforms, and providers in bike-sharing systems of Porto Alegre in Southern Brazil and Vancouver's bike-sharing in Canada. We tracked these actants using the Actor–Network Theory through 30 interviews with consumers and managers. The findings suggest a dynamic ecosystem of mechanisms that mediate interactions and enact “sustainable collaborative consumption (SCC)” through digital solutions and physical equipment. The results illustrate that SCC is positively influenced by three avenues: (1) sustainable individual actions, (2 ) digital platforms, and (3) sustainable physical equipment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".