What I learned in Attawapiskat: An Earth Science Outreach Adventure
Bibliographic record
Abstract
Earth Science Outreach in Canada takes many forms. Great programs are delivered by a multitude of individuals and organizations in a multitude of ways. And whether the reaching out is visiting your child's Grade 4 class to talk about minerals, or putting together a huge community event like Earth Sciences for Society exhibitions, these programs are driven by the recognition that youth, teachers and the public need to become more aware of complex Earth Systems, how they work and why we all need to care. It has been noted by our international friends that what we manage to do in Canada without specific government involvement and funding is pretty amazing. This unique grass-roots community of passionate, committed people is held together by a thread of common mission and the low-tech but immensely effective work of the Canadian Geoscience Education Network (CGEN). Communicating via email, members are apprised of upcoming events, opportunities and resources and kept connected with each other. Industry's growing understanding of their own role in supporting education and outreach efforts adds dimension to this collective and has proven extremely valuable; industry partnerships will be increasingly more important to future projects. In my own experience, Earth Science Outreach is never more effective than when these diverse components are brought together for larger projects. Sometimes born of crazy confluence of chance and adversity, these collaborations of folks from all areas of geoscience create some great learning opportunities for everyone involved. This article describes an excursion to the remote First Nation community of Attawapiskat, northern Ontario, brought about by a collective effort of individuals and organizations, and which epitomizes the tremendous value of such collaborations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.010 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".