Sharing economies: moving beyond binaries in a digital age
Bibliographic record
Abstract
Abstract In periods of turbulence, the tendency to simplify messages and polarise debates is nothing new. In our hyper-mediated world of online technologies, where it seems that even national policy can be forged in the 140 characters of Twitter, it is more important than ever to retain spaces for in-depth debate of emergent phenomena that have disruptive and transformative potential. In this article, we follow this logic and argue that to fully understand the diverse range of practices and potential consequences of activities uncomfortably corralled under the ambiguous term ‘the sharing economy’ requires not a simplification of arguments, but an opening out of horizons to explore the many ways in which these phenomena have emerged and are evolving. It is argued that this will require attention to multiple terrains, from diverse intellectual traditions across many disciplines to the thus far largely reactive responses of government and regulation, and from the world of techno-innovation start-ups to the optics of media (including social media) reporting on what it means to ‘share’ in the 21st century. Building on this, we make the case for viewing ‘the sharing economy’ as a matrix of diverse economies with clear links to past practices. We propose that to build a grammar for understanding these diverse sharing economies requires further attention to: (1) The etymology of sharing and sharing economies; (2) The differentiated geographies to which sharing economies contribute; (3) What it means to labour, work and be employed in sharing economies; (4) The role of the state and others in governing, regulating and shaping the organisation and practice of sharing economies; and (5) the impacts of sharing economies. In conclusion, we suggest that while media interest may fade as their presence in everyday lives becomes less novel, understanding sharing economies remains an urgent activity if we are to ensure that the new ways of living and labouring, to which sharing economies are contributing, work to promote sustainable and inclusive development in this world that ultimately we all share.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.019 | 0.038 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".