On the problem of astronomy and popular prejudices: the case of ancient astronomers and NASA funding
Bibliographic record
Abstract
Abstract The aim of this paper is to explore the popular prejudices faced by ancient and modern astronomers. In the case of the ancients, this entails examining the astronomer Thales of Miletus (ca. 620 BC–ca. 546 BC) via the analysis of various ancient texts, such as Aristotle’sPolitics, Plato’sTheaetetusand Aristophanes’Clouds. In the case of the moderns, this entails examining the National Aeronautics and Space Administration (NASA) agency via its funding history, public opinion polls, and spinoff reports. The findings suggest that NASA’s constant efforts to justify their existence in terms of practical outcomes, mirrors the experience of ancient natural philosophers who were depicted with their “heads in the clouds” and in possession of an epistemology devoid of any practical significance.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.032 | 0.073 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".