The geoscience education research (GER) community of practice: a brief history and implications from a needs assessment survey
Bibliographic record
Abstract
The geoscience education research (GER) community has evolved and grown over the past several decades. Using Wenger et al.'s Community of Practice (CoP) model (2002), we discuss how the GER CoP (which is broader than the formal discipline of GER) has changed, highlighting noteworthy events and growth points. Trends in community membership and connections are noted. Additionally, we conducted a GER community needs assessment to identify ways in which the CoP could build on its momentum. The survey included questions on CoP member demographics, engagement in GER work, and professional development needs. We received 107 responses, primarily from the United States and from individuals with geology or atmospheric science backgrounds. The survey highlighted the need for intentional outreach to international venues, K-12 teacher audiences, and underrepresented groups in the GER community. The survey also revealed the various ways in which GER CoP members engage in research, teaching, and dissemination activities. The most commonly used resources for increasing GER knowledge were the SERC site and the Journal of Geoscience Education (JGE). Respondents expressed a strong desire for professional development opportunities, including methodological training and community knowledge exchanges. Based on the survey results, recommendations are proposed to enhance the inclusivity, mentorship, and dissemination efforts within the GER community. The findings emphasize the importance of networking, expanding resources, and addressing the needs of diverse members to foster a vibrant and inclusive GER 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 | 0.028 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".