Networking in the life science sector: The missing link in British Columbia
Bibliographic record
Abstract
Several studies have identified that the Vancouver life science network faces a variety of challenges. Holbrook et al. (2003) pointed towards the little horizontal and vertical integration of the cluster and the difficulty of finding and retaining qualified personnel, due to competition from the US and Eastern Canada. Gertler and Quach (2005) emphasize Vancouver’s dependency on a leading firm, QLT Inc., which has declined in recent years. And Wixted and Holbrook (2011) conclude that Vancouver’s location will be a barrier to its development and the fact that governments, both local and national should step in. The paper identifies a management mechanism as a possible solution to most of these challenges based on European and Asian experiences in the biotechnology field. In these cases, network leadership has proven to create higher levels of collaborative and absorptive capacity – the ability to built fruitful relationships among stakeholders and gain new knowledge through those and outside links. Based on this framework, the paper analyses, which elements are missing in Vancouver and how a network manager could solve or offset some of these issues the life science field is facing.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".