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Harvey Nininger's 1948 attempt to nationalize Meteor Crater

2008· article· en· W2004652388 on OpenAlexaff
Howard Plotkin, Roy S. Clarke

Bibliographic record

VenueMeteoritics and Planetary Science · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsWestern University
Fundersnot available
KeywordsImpact craterApolloHistoryLawBeautyArt historyPolitical scienceAstrobiology

Abstract

fetched live from OpenAlex

Abstract— Harvey Nininger successfully petitioned the American Astronomical Society to pass a motion in support of nationalizing Meteor Crater, Arizona, at its June 1948 meeting. He alleged that the Barringer family, who held title to the crater, was depriving American citizens of its scenic beauty and scientific value. He then reportedly went on to make the unauthorized‐and false‐claim that the family would be receptive to a fair purchase offer for the crater. The Barringers, who had not been given advance warning of the petition and were not present at the meeting, felt ambushed. They quickly and forcefully rebutted Nininger's allegations, made it clear they had no intention of relinquishing their title to the crater, and terminated his exploration rights. What led Nininger to such a curious and self‐defeating act? Based on our reading of his voluminous personal correspondence, we conclude that it was rooted primarily in his complex relationship with Frederick Leonard and Lincoln LaPaz, and his desire to establish a national institute for meteoritical research‐with them, originally, but after a serious falling out, on his own. Prevented from moving his American Meteorite Museum to the crater rim, Nininger wondered what would happen if the crater was nationalized and made into a public park, with an accompanying tourist center and museum. With characteristic élan, he could picture himself at its head, with a secure salary and adequate space to exhibit his meteorite collection.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.236
Teacher spread0.211 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2008
Admission routes1
Has abstractyes

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