SPACE DEBRIS AND PRIVATE ACTIVITIES: Can a Private Operator Change its Licence to Reduce its Obligation to Mitigate Space Debris?
Bibliographic record
Abstract
Over the last three decades, space has been redefined.Private actors have invested heavily in space activities, with a focus on planned mega-constellations such as OneWeb and Starlink.These private projects have significantly increased the number of objects in orbit and renewed the debate on the role of private actors and their obligation to mitigate space debris.This thesis addresses the legal justification for States to license private actors and which activities need a licence.However, the primary focus is on the possibility for a private actor to change its licence -through the creation of a subsidiary -from one State to another after the object has already been launched.This change would enable the private operator to obtain more favourable terms and conditions and avoid the obligation to limit the creation of space debris, contrary to the sustainability of Earth orbits.This thesis concludes by examining whether the International Telecommunication Union could be the best venue to achieve consensus for a potential solution to the problem. of See SpaceRef Editor, "PanAmSat's new PAS-1R Satellite in Position to Power Top Video, Internet, and 1 Data Customers" SPACERef (February 20, 2001) online: .Note that SpaceX was founded in 2002, but that the importance of SpaceX arguably started with the 2 successful launch of Falcon 1 in 2008.Alison Eldridge "SpaceX" (2023) in Encyclopaedia Britannica ;"
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".