An ecosystem-based job-creation engine fuelled by technology entrepreneurs
Bibliographic record
Abstract
Job creation is at the centre of the rationale provided by governments and publicly funded organizations for investing in services purported to support entrepreneurs to launch and grow technology startups. However, little is known about how to design and build the engines that convert these publicly funded services into jobs in a region. In this article, we argue that the architecture of a job-creation engine fuelled by technology entrepreneurs is important and that it should be made visible to the stakeholders of a regional venture system. The manner in which the components of a job-creation engine are organized and integrated determines the effectiveness and efficiency of the conversion of public funds into jobs. Making visible the architecture of a job-creation engine enables individuals and organizations to: i) better understand the link between the investment made to service technology entrepreneurs and systematic job creation; ii) utilize the regional venture system more effectively; and iii) set the performance benchmark for capability improvement and rapid adjustment to environmental changes. The experience gained from operating Lead To Win since 2009 is used to describe the architecture of a job-creation engine fuelled by technology entrepreneurs that operate in Canada’s Capital Region. Lead To Win is an ecosystem designed to help a technology venture generate sufficient revenue to create six or more knowledge jobs in the region within three years of inception.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".