The politics of sharing: Sociotechnical imaginaries of digital platforms
Bibliographic record
Abstract
This paper looks at the role of sociotechnical imaginaries surrounding the governance of the sharing economy in two different locations: Canada and United States. Policy makers are trying to tackle the sharing economy without potentially creating negative impact on innovation. While much of the recent discourse around the sharing economy portrays it negatively, early peer-to-peer digital platforms were envisioned as new pathways toward grassroots, inclusive, fair and low-impact economies (Schor, 2016). However Jasanoff & Kim argue that the evaluation of the positive and negative aspects of technological change have always been influenced by specific tacit or explicit political imaginations of nations in terms of how to power modern social life (Jasanoff & Kim, 2013, p. 190). Using a comparative approach, this paper analyzes these imaginations as they are expressed through policy reports and recommendations. The study shows that the sharing economy appears to be seeking a set of diverse imaginaries including new economic freedom, sustainable consumption, decentralized society, demise of social hierarchies and regulatory freedom.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.062 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| 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".