13. Paddle Your Own Canoe: Metaphors for Teaching Between the Tides
Bibliographic record
Abstract
Nearly twenty college and university voyageurs hailing from Atlantic Canada to the Pacific Coast and points in between, as well as intrepid pedagogues from institutions of higher education from Asia and Australia rendezvous at the Small Craft Aquatic Centre in Fredericton, New Brunswick, on the shores of the St. John River. The sun shines brightly on this warm, mid-June morning, and the water sparkles, inviting the assembled paddlers to embark on a fleeting voyage of discovery in the great Canadian out-of-doors. The group leader addresses the circle of eager life vest-clad participants as they stand, paddles in hand, in anticipation of the day’s activity. “Welcome to this pre-conference workshop” begins the facilitator, “let us begin with a warm-up activity!” A casual observer of the scene would surely be perplexed: a canoe-based conference workshop activity? For professors of all ages, shapes, and sizes? What’s this about?
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".