Linking online social proximity and workplace location: social enterprise employees in British Columbia
Bibliographic record
Abstract
Online professional networks have the potential to expedite and expand the success of corporations and, especially, socially oriented enterprises – such as non‐governmental organisations (NGOs) and social enterprises, which are businesses owned and operated by a non‐profit. Research to date has not examined the extent and composition of online professional social networks among social enterprise employees nor their inter‐relationships. Specifically, the link between individual connectivity and physical workplace is not understood. The purpose of this study was to provide a geographical understanding of communication amongst social enterprise employees. In British Columbia, Canada, 358 social enterprises and their most senior staff member were located on LinkedIn. Social network analysis, geographic information system (GIS) analysis and statistical analysis revealed that senior staff which had a betweenness centrality score were more than expectedly located in workplaces within the metropolis (Greater Vancouver) and within very highly materially deprived areas within the city. Further analysis showed that the majority of senior staff that had a betweenness centrality score, or that were directly connected to a senior staff member with a betweenness centrality score, were clustered within a 65 square kilometre downtown zone in the metropolis. This suggests the existence of ‘local buzz’, ‘regional pipelines’ and a digital divide drawn along metropolitan lines. This research represents the early understanding of social networks and their role in connecting enterprises with similar (or competing) goals along the axis of space.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".