Bibliographic record
Abstract
European, Canadian, Asian and Russian responses have varied widely. Internal to the United States, public reaction has been mixed. One factor underlying the caution demonstrated by virtually all respondents is the issue of cost; such an effort is beyond the resources of a single country and will require international cooperation, i.e., investment. The second factor is the difficulty of “ensuring sustainability ” through multiple U.S. Administration and Congressional shifts. This paper briefly reviews the history of human space flight in light of the factors which have driven expenditures in the past and posits that the current initiative may fail unless one of the following conditions is met: (1) The emergence of a political imperative sufficient to spur international governments to reprogram funds while garnering cross-generational public support, or (2) the development and implementation of a economic model that enables cost-sharing among international governments, private industry, commercial interests and the public to a degree previously unprecedented in human space flight. The paper concludes with some high-level requirements for a cost model necessary to meet the second condition, a speculative discussion of the type of “transformational event ” to meet the first, and some suggestions for approaching the issue of program sustainability. I.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".