Why Astronomy is Useful and Should be Included in the School Curriculum
Bibliographic record
Abstract
Even in my astronomically-developed province of Ontario, Canada, with an excellent public education system, astronomy was not a compulsory topic in the school curriculum until quite recently. So I began collecting a list of reasons why it should be, in the hope of persuading those who set the curriculum. It would be instructive, however, to start by asking: “Why is astronomy not included in the curriculum?” Here are some possible reasons: (i) Astronomy is perceived to be irrelevant to practical concerns such as health, nutrition, agriculture, environment, engineering, and the economy in general; this is particularly true in developing countries, (ii) Most school teachers have little or no knowledge of astronomy, or astronomy teaching; in fact, they may have the same deeply-rooted misconceptions as their students, (iii) Astronomy is perceived as requiring night-time activities (“the stars come out at night, the students don’t”), and expensive and complex equipment such as telescopes, (iv) Astronomy is perceived as being solely “Western” by some non-Western cultures. (v) There may be conflict – real or perceived – between astronomy and personal beliefs such as religion, culture, and pseudo-science; in fact, astronomy is sometimes viewed as being as speculative as pseudo-science, (vi) Many of the available resources are designed for affluent schools in affluent countries, or for different latitudes, longitudes, and languages, (vii) Astronomy may be seen as allied with high technology, with all its real and perceived dangers.
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 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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".