Bibliographic record
Abstract
Story 5 2. Heaven Is Like West Edmonton Mall 11 3. What Is It about Storytelling that Gets This Ancient Blood of Mine Roaring? 16 4. Welcome to "The Greyhound" and Why Stories Matter 20 5. Disconnection 23 6.How to Be a Great Cook 26 7. Now You're Cooking 28 8. Listen and Volunteer 31 9. Embrace the Technology / Share the Stories 35 10.Camping Challenge 38 11.Being a Guest: What to Bring 40 12. Being a Good Guest: What to Do 43 13.Where Is Your Television? 45 14.Get Connecting 48 15.Be Real 54 16.Be Curious 56 17.A Good Storyteller Is Mindful of Time 58 18.The Art of the Introduction 68 19.Interviewing Your Elders to Reclaim a Good Teaching Story 71 20.What Story Do You Need for Peace?75 21.Contemporary Indigenous Storytelling and Honouring the Late Trevor Evans with Stories 87 22.The Circle of Life 94
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.780 | 0.647 |
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".