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Dark Star

2023· book· en· W4382600888 on OpenAlexfundno aff
Matthew H. Hersch

Bibliographic record

VenueThe MIT Press eBooks · 2023
Typebook
Languageen
FieldPhysics and Astronomy
TopicSpace exploration and regulation
Canadian institutionsnot available
FundersNational Air and Space MuseumUniversity of OxfordYork UniversitySmithsonian InstitutionUniversity of PennsylvaniaJohnson Space CenterJohns Hopkins UniversityMarshall Space Flight CenterNational Aeronautics and Space AdministrationU.S. Department of Defense
KeywordsSpace ShuttleHuman spaceflightAeronauticsSpace (punctuation)Rocket (weapon)Space explorationSpace AgeEngineeringOperations researchComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.365
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.253
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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