Bibliographic record
Abstract
I reflect on my half-century of experience in astronomy outreach in Canada and beyond, on the organizations in which I have served, and on the factors that have contributed to success in outreach. One factor is partnership that builds on the strengths of two or more individuals or organizations, such as the pro-am partnership in Canada during International Year of Astronomy. Challenges remain: the lack of a science “culture” in Canada; the lack of funding for outreach; the low priority of outreach in many parts of the professional community; and the difficulties in reaching new and diverse audiences—especially underserved ones. For success, there is a strong need for astronomy outreachers to seek high-impact, high-leverage strategies, to make themselves aware of “best practices” in outreach, to make training part of their outreach activities, and to strive constantly for improvement by evaluating and/or reflecting on the results. I attribute my own modest successes to my mentors and role models, to the help of colleagues, students, and partner organizations and their members—not the least being the RASC—and to the sheer enjoyment of communicating astronomy to diverse audiences. This article is based on a presentation given to the Canadian Astronomical Society on 2012 June 6, on the occasion of my receiving the inaugural Qilak Award, for my work in communicating astronomy to the public.
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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.053 | 0.055 |
| Scholarly communication | 0.030 | 0.023 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.014 | 0.043 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".