The <i>Astronomy Education Review</i>: report on a new journal
Bibliographic record
Abstract
: The Astronomy Education Review , “a lively compendium of research, news, resources, and opinion” about astronomy and space science education, has now been in operation since 2002. This paper discusses why such a journal/magazine was needed and describes the key decisions that were made about how to implement it: electronic publication to make the journal widely accessible while keeping operations costs low; achieving quality comparable to a paper journal through rigorous refereeing, copy-editing, and consistent style; fixed text after publication; and long-term availability. The topics covered in the issues published to date are summarized, and we assess the impact of the journal. The journal itself can be found at http://aer.noao.edu. Introduction The Astronomy Education Review (AER) , a new electronic journal, was established to serve the growing community of researchers and educators who are active contributors to astronomy and space science education. For decades, it has been a cause for concern that too few US undergraduates are being trained in science, engineering, and mathematics (NCEE, 1983). In addition, our increasingly technological society requires citizenry that is at least somewhat literate in science. As a consequence, National Aeronautics and Space Administration (NASA) and National Science Foundation (NSF) have committed very significant resources toward improving both access to, and the quality of, science education at all levels. Proposals to the NSF are now judged not only on the intellectual merit of the proposed research but also on its broader impacts, including how well the work will advance “discovery and understanding while promoting teaching, training and learning” (NSF, 2004).
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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.032 |
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".