Bibliographic record
Abstract
What is firstly considered here is whether co-working, the ‘Pooling’ of small to medium-sized businesses in specifically designed buildings housed within clusters is a significant new way of working. Second, whether the construct supports the economic growth of small to medium-sized firms. SMEs are a critical element of the internal fabric of clusters. 95% of businesses in London, UK are SMEs defined as firms with fewer than 250 employees. It is argued that co-working spaces increase cultural transference between firms and assists trust relationships to form. Trust and increased organisational-cultural understanding is particularly useful for cross-sector working. The inherent knowledge-building and innovation-focused pro-social environment of the construct grows in value as more people join its community of practice, evidence of Network Effects. Co-working is a site of cross-sector cultural negotiation where the incubation and acceleration of novel products, services or experiences is the aim. This is of interest to media firms who are beginning to blend content with technology, scholars interested in organisational culture and policy-makers. Co-working has become an international phenomenon therefore it’s worthy of study but has received little scholarly attention. The empirical basis is a three-year study ‘Organisational Culture of Public Service Media in the Digital Mediascapes: People, Values and Processes” (2015–2019)’ (Glowacki & Jackson) looking at the organisational culture of ten high technology clusters. 150 interviews, ‘city walkabouts’ and grey literature were collected (2016-18). The study aims to assist public service media to understand how to partner with other sectors in a media landscape influenced by high-end technology and network distribution. The element of the project offered here specifically looks at co-working which emerged as a significant organisational phenomenon within high technology clusters, the focus is on the City of Toronto.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".