Understanding the Sharing Mania: What Libraries Can Learn from the Current Rise of Collaborative Consumption Models
Bibliographic record
Abstract
This article explores the current rise of collaborative consumption models for a library context. Specifically, it explains these models and unpacks key success factors from collaborative consumption. Subsequently, the article postulates what is beneficial for librarians and libraries to understand from the current trend of collaborative consumption. While there has been much research and criticism about applying traditional business models to libraries, little academic work has been completed in using contemporary business practices such as the collaborative consumption models. To address this gap in the literature, this article addresses the following questions via a literature review: “What is the current trend of collaborative consumption?” “What models for success exist within collaborative consumption?” “How do libraries already fit into collaborative consumption?” “What are the ramifications of considering libraries a form of collaborative consumption?” First explored is the definition of collaborative consumption and its current state. Following this query is a look at what models exist within collaborative consumption and how these models have contributed in making collaborative consumption successful. Finally, there is a discussion where librarians and libraries fit into these models. Areas of future research in this field are also identified with the hope that librarians and libraries can use these disruptive business models as momentum to enhance their services and resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".