The legal framework related to the privatization and commercialization of remote sensing satellites in the United States and in Canada /
Bibliographic record
Abstract
This Thesis deals with the national legal aspects of a particular space application: remote sensing by satellites, also referred to as earth observation systems. Governments have been the leading providers and users of satellite imagery data since the advent of earth observation satellites (i.e. almost 40 years ago). However, this has changed, particularly in the United States, with several private companies having acquired and launched their own imaging satellite systems. This new trend towards commercialization and privatization of the remote sensing industry, which appeared firstly in the United States and which is now being extended to Canada, required a change in policy. The role played by the government policies and regulations in shaping the prospects for the emerging commercial remote sensing satellite firms is of critical importance. In this context, these policies and regulations will determine the conditions that will enable commercial firms to realize their competitive potential in both the domestic and international marketplace. In this Thesis, a brief overview of the technical and historical legal backgrounds of remote sensing is provided. Then, the international legal framework of remote sensing is briefly analyzed. Finally, a thorough analysis of the policies, laws and regulations applicable within the United States and Canada is presented.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".