Losing Our Dark Skies: The Space-Biased Medium of Satellite Megaconstellations
Bibliographic record
Abstract
This article examines the technological risk of communications satellites assembled into globally spanning arrays known as megaconstellations. SpaceX’s Starlink system is by far the largest, with the company having deployed several thousand units in low Earth orbit and planning to launch tens of thousands more. Starlink has been the subject of media scrutiny as light reflecting off these satellites, and the background electronic noise they emanate, impedes astronomical observation. Such infrastructure in increasingly crowded orbital shells is at a heightened risk of collision, which can break into smaller fragments and cause the proliferation of orbital space debris. The consequences of mounting economic pressures for satellite technologies is that Earth’s skies will be increasingly diffusely brightened, obscuring the cosmos and the stars. Without effective regulation on access to space and orbital debris, the dark nighttime sky, which has been shared for all of history, is threatened. To analyze this unfolding possibility induced by technological innovation, this article draws from Harold A. Innis’ theory of space-time bias to contend that communications satellite megaconstellations are a result of our modern civilization’s fixation with the present moment.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".