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’s Politics , Plato’s Theaetetus and 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".