Bibliographic record
Abstract
Winner, Northwords Book Award 2022 Short-listed, Saskatoon Public Library Indigenous Peoples’ Publishing Award 2022 Stories are medicine. During a time of heightened isolation, bestselling author Richard Van Camp shares what he knows about the power of storytelling—and offers some of his own favourite stories from Elders, friends, and family. Gathering around a campfire, or the dinner table, we humans have always told stories. Through them, we define our identities and shape our understanding of the world. Master storyteller and bestselling author Richard Van Camp writes of the power of storytelling and its potential to transform speakers and audiences alike. In Gather , Van Camp shares what elements make a compelling story and offers insights into basic storytelling techniques, such as how to read a room and how to capture the attention of listeners. And he delves further into the impact storytelling can have, helping readers understand how to create community and how to banish loneliness through their tales. A member of the Tlicho Dene First Nation, Van Camp also includes stories from Elders whose wisdom influenced him. During a time of uncertainty and disconnection, stories reach across vast distances to offer connection. Gather is a joyful reminder of this for storytellers: all of us.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.779 | 0.543 |
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".