Measuring Collaboration Mechanisms in the Canadian Space Sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it