Can Technology Clusters Deliver Sustainable Livelihoods? Constructing a Role for Community Economic Development
Bibliographic record
Abstract
The sustainability of technology clusters has becomequestionable as a result of recent declines in technology relatedjobs.This research suggests that community economic development (CED)strategies can aid in sustaining and stabilizing technology sector jobs andmarkets.Focusing on Canada's National Capital Region (NCR), the limitsand advantages of utilizing CED strategies in addition to government andbusiness strategies that encourage cluster growth is explored. Past research regarding the development and growth of technology clusters,as well as limitations associated with these clusters, is presented.Theperformance of the NCR is discussed, highlighting areas of both positive andnegative growth.The role of CED is then analyzed, focusing on some of theadvantages and disadvantages of this program and its prevalence among thescience and technology industry.CED performs the followingfiveroles among technology clusters: (1) bridging the digital divide; (2)facilitating knowledge workers to manage technology-sector volatility; (3)mobilizing organizational resources that promote community development amonglow-income people; (4) producing multi-sector leadership structures; and (5)encouraging the development of community-owned science and technologyenterprises. CED is vital for technology-cluster growth.Current strategies beingused in Ottawa (restructuring the governance of economic-development and thescale of training) are considered, as are predictions regarding the future ofCanadian technology clusters. (AKP)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".