Knowledge of astronomical scale: Measurement and evaluation
Bibliographic record
Abstract
Having an appreciation for astronomical scale is essential for understanding the foundations of astronomy. However, a key obstacle in developing this understanding is the lack of direct ways to acquire this knowledge. Personal experience may even be detrimental, given that our direct experience is of the Earth as something extremely large, whereas stars, for example, appear as tiny pinpricks of light. As a first step to address this issue, it is necessary to assess people’s knowledge of astronomical scale to identify common misconceptions and evaluate the effectiveness of educational interventions. Previous instruments have generally only included a few questions about scale—mostly through multiple choice—limiting the number of objects simultaneously probed to three and often not probing all possible rankings. To measure people’s knowledge of astronomical scale, we developed an instrument that allows for easy collection, analysis and presentation of data ranking multiple astronomical objects. I will present this instrument and the results from three different samples before and after astronomy instruction: middle school students (N = 922), pre-service science teachers (N = 41) and visitors to a public guided astronomy night viewing tour (N > 500).
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 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 | 0.002 | 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 | 0.000 | 0.000 |
| 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".