Bibliographic record
Abstract
In this chapter, two case studies, involving university students in St. John’s Canada, and Plymouth England, are presented. The first case examines how the tutor utilises the seascape of the island of Newfoundland to nurture a deeper sense of place, as well as adventurous learning, in undergraduate students. Building on strong visual representation of this rugged North Atlantic island, students capture and explore their learning experiences of the seascape in visually based learning portfolios. These are powerful reflective tools that both tutor and students utilise to further cross-curricular connections to the seascape, maritime culture, and places of historical significance. In Plymouth, Place based outdoor education is an undergraduate class. Drawing on place-responsive outdoor education, literature students are provided with opportunities to collectively explore the rich maritime heritage of Plymouth and investigate their attachment to place. Visual methods are utilised in teaching and assessment, as are lived experiences, photographs/videos, as well as historical images. Sailing in Plymouth Sound, and exploring the land from the water, allows students to experience similar seascapes as Drake, The Pilgrim Fathers, Cook, and Darwin. Using photo-elicitation allows students in both contexts to make meaning from their experiences of seascapes.
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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 | 0.000 | 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 | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 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".