Bibliographic record
Abstract
Purpose This paper aims to categorize the typologies of co-working spaces and describe their main characteristics. Design/methodology/approach The aim is reached by means of analyzing 15 co-working spaces located in the capital area of Finland. The data used consist of interviews, websites, event presentations and brochures. Findings As a result, six co-working space typologies were identified: public offices, third places, collaboration hubs, co-working hotels, incubators and shared studios. The categorization was made by using two axes: business model (for profit and non-profit) and level of user access (public, semi-private and private). Research limitations/implications The results provide a viewpoint on how co-working spaces can be categorized. Practical implications In practise, the results can be applied by all stakeholders who are working with alternative workplace solutions to respond to the needs of new ways of working, especially via workplace services for multi-locational and flexible working, including facilities managers, corporate real estate executives and designers. Originality/value This research builds on the previous academic literature on co-working spaces by making the phenomena more explicit for researchers and practitioners who are facing the challenges of developing new alternative workplace offerings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| 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".