You Manage What You Measure: Achieving Space Sustainability and Self-Regulation of the Outer Space Industry Through Environmental, Social, and Governance Corporate Disclosure
Bibliographic record
Abstract
The prospect of self-regulation for the space sector is discussed in this study. The article is divided into three sections. The Overview section offers an outline of international guidelines and industry standards and the benefits they provide on controlling commercial use of space. The Case Study and Implications for the Commercial Use of Space section examines how publicly listed corporations disclose outer space activities in accordance with international guidelines and standards. This section offers insights on disclosure practices from U.S. and non-U.S.-based corporations that are found in the Procure Space Exchange Traded Fund. Some of the corporations included in the Exchange Traded Fund are Dish Network Corporation, Garmin Ltd, Sirius XM, Virgin Galactic Holdings, Inc., Lockheed Martin Corporation, Boeing, and Echostar Corporation. The Case Study and Implications for the Commercial Use of Space section highlights that the commercial use of space is not widely considered by international guidelines and industry standards. The Recommendations and Challenges section concludes by not only offering recommendations but also recognizing future challenges for space sustainability .
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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".