Coworking Spaces: Catalysts of Workplace Revolution and Organizational Evolution
Bibliographic record
Abstract
This symposium delves into the relevance, mechanisms, and transformative potential of coworking spaces. These shared work environments, equipped with essential infrastructure, attract individuals, start-ups, and established firms, serving as hubs for knowledge exchange, entrepreneurial activities, as well as innovation. By fostering collaboration mainly through physical co-presence, coworking spaces align with the open innovation paradigm while emphasizing community dynamics over organizational boundaries. Unlike incubation centers, coworking spaces cultivate vibrant ecosystems that facilitate resource sharing, knowledge exchange, and entrepreneurial opportunity recognition through collective reflection and inspiration. However, they also pose competitive risks such as opportunistic behavior, highlighting a need for maintaining a delicate balance between collaboration and competition. Despite their growing prominence, scholarly exploration of coworking spaces remains still limited. This panel symposium aims to advance the understanding of coworking spaces as catalysts for entrepreneurial activities and organizational change. By critically examining how various types of coworking spaces shape cooperation, entrepreneurial activities, and transformative processes, the symposium will explore their role in balancing the complexities of shared value creation and competition.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".