Bibliographic record
Abstract
“Urban Sharing in Toronto” explores the landscape of the sharing economy in the city context. This research is a result of a Mobile Research Lab conducted by 8 researchers from Lund university in 2019. Specific focus is on three sectors: sharing of space, mobility and physical goods. For each sector, we discuss the drivers and barriers to the sharing economy, the associated sustainability impacts, the potential impacts on incumbent sectors, and the institutional context of sharing. Then, attention is turned to the role of the city council in engaging with the sharing economy and specific governance mechanisms employed by the city council are described. Since the sharing economy is not sustainable by default, urban sharing organisations, city governments and incumbents all have important roles to play in ensuring that the sharing economy positively impacts cities and their citizens. In the face of negative perceptions and possible impacts of the sharing economy, we may need to be more deliberate in thinking in terms of scaling the sharing economy to the size, needs, and capacities of cities. In this report we provide five recommendations to the City of Toronto and its citizens.Insights contained within this report may support the City of Toronto and other Sharing Cities, as well as urban sharing organisations and third-party actors in Toronto and beyond in their strategic work with the sharing economy for sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".