Measuring Collaboration Mechanisms in the Canadian Space Sector
Bibliographic record
Abstract
Abstract Innovation in space science and technology involves interactions among players from the public and private sectors. Interinstitutional and intersectoral collaborations have been proven to stimulate innovative activities and improve their outcomes in many activity sectors. The government of Canada, including its designated agency for space-related affairs, the Canadian Space Agency (CSA), is one of the major players in the Canadian space sector and has played an important role in encouraging these collaborations. Consequently, Canadian government organizations emphasize the importance of interinstitutional collaboration in accelerating innovation, promoting spin-offs, and ensuring sustainable funding for research and innovation programs. How should collaborations be measured, reported on, and evaluated? Measuring the extent of collaboration is challenging due to the variety of collaboration mechanisms and the degree to which organizations report on their interactions. The space sector also has specificities that call for a distinct methodology: the culture of secrecy, publication practices, the competitive advantage of certain collaborations, the limited funding available, and so on . This article will present a methodology for studying collaborations in the Canadian space sector using bibliometric data, surveys, and publicly available CSA contract data. Mapping these datasets will help identify the extent of interinstitutional collaborations, cross-fertilization between terrestrial and space research, and the impact of CSA funding on research outputs. Results from three case studies will be presented: Space Medicine and Life Sciences, Space Robotics and Rovers, and Earth Observation. Impact measurements not only play an important role in justifying stakeholders' investments, but also help clarify the innovation patterns and efficiency of the various mechanisms used.
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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 | 0.018 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.082 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".