Anti-satellite weapons : threats, laws and the uncertain future of space
Bibliographic record
Abstract
L'auteur a accord une licence non exclusive permettant la Bibliothque et Archives Canada de reproduire, publier, archiver, sauvegarder, conserver, transmettre au public par tlcommunication ou par l'Internet, prter, distribuer et vendre des thses partout dans le monde, des fins commerciales ou autres, sur support microforme, papier, lectronique et/ou autres formats.L'auteur conserve la proprit du droit d'auteur et des droits moraux qui protge cette thse.Ni la thse ni des extraits substantiels de celle-ci ne doivent tre imprims ou autrement reproduits sans son autorisation.Conformment la loi canadienne sur la protection de la vie prive, quelques formulaires secondaires ont t enlevs de cette thse.Bien que ces formulaires aient inclus dans la pagination, il n'y aura aucun contenu manquant.First, 1 must give the most credit and appreciation to the United States Air Force for making my year of study at Mc Gill University possible.1 give special thanks to Colonel Ronald R. Ratton, a mentor, supervisor and friend who encouraged me to apply for the LL.M. pro gram in Air and Space Law and supported my application for admission into the program.1 thank my wife, Trenna, and our children, Bryce and Raleigh, for uprooting their lives once again to follow me from assignment to assignment.Moving so frequently can be hard, especially when it is done so often. 1 love and appreciate themall the more for their love, understanding and support.Trenna also merits appreciation for
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".