Legislative Reform to Foster a Sustainable Orbital Launch Industry in Canada
Bibliographic record
Abstract
Canada’s orbital rocketry industry is underdeveloped and governed by outdated and unclear laws that fail to attract both investors and launch providers to the market. This thesis contends that improving existing laws will provide greater certainty and clarity to investors, business leaders, and other stakeholders, in turn drawing investment to this nascent Canadian industry. An increase in investment will accordingly offer economic and social benefits to local communities and Canadians. As a product of this study, the author’s draft bill, titled the Space Transportation Authorization and Registration Act (STAR Act), illustrates the legislative improvements recommended to the Canadian federal government. The STAR Act is provided in full in chapter 5 and was drafted as a tool to bring about change in the nearly non-existent orbital rocketry industry in Canada. It honours obligations imposed on Canada at international law and learns from other domestic and foreign space-related legislative and regulatory frameworks. The STAR Act has been tailored specifically to the Canadian context, namely by leveraging Canada’s existing infrastructure as well as vast amounts of uninhabited land and lengthy coastlines, and by acknowledging the considerable barriers to entry in this capital-intensive industry. The draft bill will guide stakeholders on the operational procedures and regulatory oversight of this industry in Canada, is written in plain language to ensure understandability, and offers flexibility and adaptability to changes within the operating environment. Overall, the STAR Act was drafted to balance legal specificity with simplicity, and will facilitate the industry’s safe, secure, and sustainable growth and development while minimizing bureaucratic “red-tape”.
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.
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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".