Bibliographic record
Abstract
Many parts of this book have been written in the temperate rain forest of Canada's West Coast, amid their soggy and vibrant communities.Specifically, many pages have been crafted alongside the Alouette River itself and surrounded by Water Strider, Step Moss, Sword Fern, Pacific Wren, Rough-Skinned Newt, Red Huckleberry, Douglas Squirrel, Skunk Cabbage, Red Cedar, and Chum Salmon, to name just a few.Many more pages have been conceptualized and penned in the presence of Harbor Seals, Pink Salmon, Glaucus Gull, Bald Eagle, Purple Sea Star, Gooseneck Barnacle, Bull Kelp, Hermit Crab, and the generous Pacific Ocean.These places and beings brought us inspiration, gifted us with ideas, and made pos si ble other ways of knowing and understanding our responsibilities to writing this book.They are our co-teachers and collaborators throughout this book.In similar ways, this book has also been created in companionship and collaboration with Cypress, Golden Ears, and Steele Mountains, Hardangerjøkulen Glacier; in the shadows of Red Cedar, Hemlock, Broad-leaf Maple, Red Alder, Douglas Fir, and Sitka Spruce; and amid the gifts of Sword Fern, Deer Fern, Lady Fern, Licorice Fern along with Salmon Berry, Black Berry, Huckle Berry, and Blue Berry.We are grateful for the microworld teachings of mosses, lichens, and fungi and to all the twittering, strutting, slipping, and hopping feathered beings and amphibians and reptiles-too vast in number to name.We are grateful to all the four-legged and furry ones who have left tracks, scents, inspirations, and lessons for us along the way.And we express gratitude to all those whirring, fluttering, plodding, scurrying, sliding smaller beings of earth and sky and the stones, soil, Sun, and weather that hold and protect them and us along the way.Our engagement with these beings has occurred on the territory of Indigenous Nations, in par tic u lar the Coast Salish and we are so thankful to the Elders, teachers, and community members who have spent time with the students and patiently listened as we fumbled around in search of moments of understanding.Without their stewardship, guidance, and incredible generosity in terms of patience, shared story, time spent with learners the work and these lands in fact would be the lesser for all.As authors, we write from our perspectives as white settler scholars and educators trying to live in better relationship with the unsurrendered and traditional territories of Shishalh, Katzie, Kwantlen, and Snuneymux peoples in the place colonially known as British Columbia, Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
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".