COVID‐19 field instruction: Bringing the forests of British Columbia to students 8,000 km away
Bibliographic record
Abstract
Abstract Field instruction is a crucial component of natural sciences education. The COVID‐19 pandemic has shifted many university courses to an online format, significantly impacting field instruction. FRST 350, Foundational Field School, is an 8‐day University of British Columbia Forestry field course taught to incoming transfer students from partner universities in China. In August 2020, I taught this course online to students studying remotely. In re‐developing the course, I spent 9 days in the field filming high definition (HD) video, 360° video, and 360° photography to best recreate the course in a short time‐frame. A 360° video records omnidirectionally, allowing viewers to “look around” in all directions, resulting in a highly immersive experience. Students expressed favorable opinions of the course, especially traditional HD and 360° video. Students generally preferred HD videos over 360°, though this was due mostly to the high bandwidth needed for 360° video and the fact that core course content was primarily conveyed as HD videos (in recognition of bandwidth challenges), with supplementary 360° videos. Students favorably noted the interactivity and immersive feel of 360° videos and photographs. This technology is financially and logistically feasible for use in a natural sciences course. Instructors engaged in online field instruction should weigh the strengths and weaknesses of various technologies, including 360° video, when determining how to best meet their learning objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".