Borrowing Spaces: The Geographies of ‘Libraries of Things’ in the Canadian Sharing Economy
Bibliographic record
Abstract
Abstract Over the last decade, the sharing economy has resulted in numerous innovative sharing and borrowing practices, many of which have the potential to radically transform communities and societies. One such innovation is the Library of Things (LoT), a non‐profit sharing space modelled on traditional library systems, which enables users to borrow a diverse range of equipment, tools and goods. This paper contributes to the emerging literature and research on the non‐profit sharing economy and the role of LoTs as sharing spaces. Drawing on in‐depth interviews with LoT founders and managers, three main socio‐spatial themes are discussed in the development of Canadian LoTs: sharing cultures, sharing capital and sharing politics. Overall, this work highlights that the success and sustainability of these sharing spaces hinge on the negotiation of these complex social and spatial dynamics, ranging from their capacity to build spaces of collaboration, experimentation and community.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".