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A Universe of Earths

2025· book· en· W7165843907 on OpenAlexaff
D. Kyle Danielson, Christopher M. Graney

Bibliographic record

Venuenot available
Typebook
Languageen
FieldPhysics and Astronomy
TopicSpace Science and Extraterrestrial Life
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPlanetEarth (classical element)UniverseExtraterrestrial lifeFigure of the EarthPlanetary habitability

Abstract

fetched live from OpenAlex

Abstract Planet Earth. The phrase trips lightly from our tongues. Yet planet Earth has been a concept for a mere fraction of recorded history. Until the mid-1600s, most humans thought of Earth as, well, just Earth—immobile, not (like the planets) participating in what Galileo called “the dance of the stars.” A Universe of Earths recounts how that all changed, and how the change augmented and enriched our understanding of where Earth and its inhabitants are in the Universe, how we fit into the Big Picture of the Cosmos. But almost as soon as humans started to grasp that Earth is a planet, many began wondering if perhaps the other planets might be earths. This bold conjecture ignited the whole history and literature of space travel, of extraterrestrials, of other worlds. Yet the thesis that the Universe is full of other worlds like our Earth has from the start been fueled much more by imagination than by evidence. For all its appeal, it has always been undermined by observations of the actual Universe. A Universe of Earths offers a surprising alternative to that “many worlds” tale, one that releases humans from the pre-Copernican view of Earth as low, lowly, dark, a cosmic sump—as well as from the persistent modern aspersion of Earth as cosmically ordinary, “mediocre.” Instead, the true Copernican picture offers the bracing realization that Earth is, in the classical sense, a star, a dynamically wandering one, and a bright, maybe even peerless participant in the dance of the stars.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.014
Scholarly communication0.0070.009
Open science0.0010.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0260.007

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 head0.010
GPT teacher head0.234
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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