ClusterOS Diagnostic Profile
Greater Glasgow Life Sciences
Greater Glasgow Life Sciences draws £1.07bn of UKRI lead-led funding across 4,329 grants, anchored by Glasgow (31%), Strathclyde (20%). 29 Companies House-traced spin-outs translate to £37m UKRI per spin-out.
The cluster shows medium-confidence "Extracting without reinvesting" and "Forgiving instead of redesigning" behaviour — extraction events accumulate without triggering structural redesign of support mechanisms, leaving value retention to recur rather than flow forward into new corporate formation. Pipeline 1 evidence: company acquisitions and exit transactions cluster 2020-2022 alongside 35 new company registrations 2007-2025, with no patterns evidencing redesign of support mechanisms following the 14 venture-funded companies' performance trajectories.
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Same data examined through five diagnostic lenses — Pipeline, Leverage, Triple Helix, Throughput, Collaboration. The interactive diagnostic is currently in private preview.
Sources: UKRI Gateway to Research (grants, outcomes); OpenAlex (publications); Companies House (spin-out lifecycle); DSIT (cluster mapping); Public investment data. Snapshot May 2026.
Stabilisation stacks · Why single interventions fail
Value extraction events generate narrative about ecosystem success; narrative legitimises continued extraction by framing it as ecosystem contribution; uncertainty about whether extraction is harmful absorbed by the success narrative.
Intermediaries produce narrative about their facilitation role; narrative legitimises intermediary existence and funding; uncertainty about direct coupling absorbed by narrative rather than demonstration.
"If ecosystem reports (e.g., from Scottish Enterprise, Life Sciences Scotland, or GCID) separated "value created" metrics (spin-offs formed, equity raised, patents filed) from "value retained" metrics (companies headquartered in cluster, employment in cluster, procurement from cluster suppliers), it might reduce the system's ability to absorb success and uncertainty signals through aggregated success narratives without making the distinction between extraction and retention visible and testable."
Leverage hypotheses are testable perturbations, not prescriptions. Where demand-side behaviour is weakly visible, the correct move is observation — improving visibility before attempting change.
Structural resemblances · Clusters with similar stall configurations
A full ClusterOS diagnostic adds actor questionnaire data, working sessions, and anchor interviews — producing higher-confidence stall identification, board-ready stack analysis, and leverage hypotheses calibrated to your specific context.