Investigating Users’ Perspectives of Coworking Space: Cases of Bangkok CBD
Bibliographic record
Abstract
Coworking space has changed working style from traditional office space to a shared working environment with different natures of knowledge professionals and industry. This style of work space has been increasingly popular during the past decade in major cities across the globe including the central business district (CBD) of Bangkok, Thailand. Users can come from various ages and environments including: students, corporate remote workers, and independent workers/freelancers. The initial investment of this type of business is not complex. Hence, the survival of this type of business is not easily guaranteed because of not only high competitiveness but also unclear information from the user side. Entrepreneurs of coworking spaces need to understand the characteristics and perspectives of users which may differ in various regions and cultures. This research will investigate the users of coworking spaces who are independent workers about their demand behaviors and perspectives on the businesses’ operations as essential factors in choosing a working space. The scope will cover users of coworking spaces in Bangkok CBD, Thailand. Research approach will apply quantitative analysis through questionnaire surveys during November, 2017. The research results will be beneficial to the entrepreneurs of coworking spaces to understand the needs of target users and to support their businesses investment strategic.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".