Bibliographic record
Abstract
Before embarking on our careers as academics, we were canoe tripping guides at the same summer camp.There, we had the privilege of spending our summers paddling and portaging with groups of teenage kids through some of the most beautiful parts of northern Ontario and Saskatchewan.Maps and route logs from past trips helped us find our way through the intricate lake and river systems, lily-filled marshes and bogs, and rugged portage trails.For this book, we had no such map.When we first envisioned it, we were not certain what exactly it might become.What we did know was that we wanted to expand and vary our conversations around a cherished national icon (that had once been our lives and livelihoods) by bringing together a range of voices and perspectives on the canoe' s diverse political meanings.We also wanted to bring these conversations to a wider audience of canoeists and readers, beyond the academic realms we both now inhabit.(As one of Bruce' s favourite paddling partners had recently put it: "when are you going to write something that I can read?")The result is this collaboration with a diverse group of authors, and we are quite pleased with what it has become.The voices in this book come not just from academia, but also from the worlds of canoe builders, paddlers, and Indigenous community leaders, elders, and knowledge keepers.These contributors bring a broad range of skills, disciplines, experiences, and histories to bear on the question of the
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.069 | 0.013 |
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".