Bibliographic record
Abstract
In the era of the digital economy and the cities’ infatuation for new creative spaces, we see the creation (either led by a public or a private initiative) and the rise in popularity of new forms of organizations for economic, knowledge and social exchanges. Collaborative spaces, also known as 'co-working spaces', are among these organizations. However, it remains unclear how many of these collaborative spaces can ensure their viability, which may depend on several reasons such as the difficulty to reach a critical mass of users that is enough, to cover their expenses and generate profits. Hence, the following questions are raised: How do collaborative spaces become viable? What are the criteria that determine the success or failure of collaborative spaces? To date, such questions are not addressed in the existing research on collaborative spaces. We rely on case studies to uncover the qualitative and non-financial performance criteria associated with their practices as well as the users’ perceptions of the value created by the collaborative spaces. Our findings contribute to the existing literature by providing new criteria for monitoring their performance. In addition, they provide to the managers and the founders of these collaborative spaces additional management tools to attract and retain users, therefore increasing their ability to reach the critical mass of users for their viability. Our results also provide public policy makers with new tools for a better understanding of collaborative spaces’ success criteria. Hence, they can refine their strategies related to the creation and support of collaborative spaces, as well as their policies for supporting entrepreneurship and creative cities.
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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 | 0.027 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".