Achieving Inclusive Field-based Education: Results and Recommendations from an Accessible Geoscience Field Trip
Bibliographic record
Abstract
Learners with disabilities are often denied field-based learning experiences in naturalistic disciplines. Geology can present substantial barriers due to rugged terrain in difficult-to-reach locations. In 2014, a field trip was executed with the dual purpose of 1) designing inclusion in field learning and 2) demonstrating to college faculty an accessible field experience. Direct observations of participants on the trip, as well as pre- and post-trip focus groups, illuminate the student and faculty field learning experience. Geoscience faculty have little guidance or support in understanding what disability is, how to reconcile accommodation with field-geology learning goals, and they cited instances where disability service providers acted as gatekeepers. The net effect of these ontologies is to reduce faculty empathy with, and thus their ability to be inclusive of, students with disabilities in field settings. Recommendations for teachers include taking campus disability-services administrators on field trips, opening and maintaining communications with disability service providers, and designing pedagogically sound field trips that align as much as possible to principles of universal design. An advocacy approach is described, which focuses on the students and the educational process, instead of on institutional compliance. Finally, geoscience faculty should conceptualize disability service providers as accessibility service providers.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".