Bibliographic record
Abstract
A captivating history of NASA's Space Transportation System—the space shuttle—chronicling the inevitable failures of a doomed design. In Dark Star, Matthew Hersch challenges the existing narrative of the most significant human space program of the last 50 years, NASA's space shuttle. He begins with the origins of the space shuttle: a century-long effort to develop a low-cost, reusable, rocket-powered airplane to militarize and commercialize space travel, which Hersch explains was built the wrong way, at the wrong time, and for all the wrong reasons. Describing the unique circumstances that led to the space shuttle's creation by President Richard Nixon's administration in 1972 and its subsequent flights from 1981 through 2011, Hersch illustrates how the space shuttle was doomed from the start. While most historians have accepted the view that the space shuttle's fatal accidents—including the 1986 Challenger explosion—resulted from deficiencies in NASA's management culture that lulled engineers into a false confidence in the craft, Dark Star reveals the widespread understanding that the shuttle was predestined for failure as a technology demonstrator. The vehicle was intended only to give the United States the appearance of a viable human spaceflight program until funds became available to eliminate its obvious flaws. Hersch's work seeks to answer the perilous questions of technological choice that confront every generation, and it is a critical read for anyone interested in how we can create a better world through the things we build.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.708 | 0.583 |
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".