A reflection on Deep Tech Innovation Process: A Case Study on Instructors and Participants of a University Educational Program
Bibliographic record
Abstract
The modern economies are driven by innovation to create key differentiators. As a result, Deep Tech that is being developed in academic and research institutions is receiving more attention as a potential new source of economic development as well as ways to solve industrial and societal challenges. In this paper, we will reflect on our experience to foster Deep Tech Entrepreneurship at the University of Saskatchewan. We will reflect on the progression of how the program has been set up and has evolved over time, the challenges and need for a common language, progress to develop the entrepreneurs, as well as how collaboration with broader non-academic communities was used to enhance the program. This paper aims to provide an overview of the program with the goal of fostering more conversations around best practices for entrepreneurial development.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".