North America’s First Geopark – Education & Communication in the Geopark
Bibliographic record
Abstract
Since joining the Global Geoparks Network in 2010, Stonehammer Geopark, New Brunswick, Canada has been piloting projects within select schools to determine the needs of students and educators related to enhancing earth science literacy. Within the geopark there are over 35 schools, over 25,000 students and more than 150 educators. A comprehensive and diverse education committee was established prior to Stonehammer joining the network. Through their dedication pilot projects were established and needs determined with engaged educators.Partnership is the cornerstone of Stonehammer's diverse activities. In its education efforts the geopark has developed a new partnership with Mining Matters, a charitable organization dedicated to bringing knowledge and awareness about Canada?s geology and mineral resources to students, educators and the general public.Stonehammer Geopark and Mining Matters will collaborate during the 2013-2014 school year to bring three deliverables to the education sector; a capacity building train-the-trainer session for in-classroom delivery beyond the partnership, a two-day teacher's workshop focusing on classroom activities and field visits, and two weeks of in-classroom activities for students and teachers hosted by Mining Matters and Stonehammer Geopark. This partnership provides a great opportunity for building local awareness of the geopark and its commitment to the community.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.008 |
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".