Bibliographic record
Abstract
I have dedicated this book, first, to the many men and women who shared their stories with me.Their willingness to open their experiences, and thus themselves, to me not only made this book possible but was also integral to the knowledge and understanding I have sought to communicate in these pages.Second, I have also dedicated this book to my nephew, Sana'a Christensen-Blondin.During the course of writing, we lost him very suddenly, and grief 's heartache was a constant companion as I rounded the final bend in seeing this project to its completion.Grief is also a persistent theme in this book, as the many stories of homelessness shared here are also stories of grieving for family, grieving for community, grieving for home.When Sana'a was only six months old, I took him and his sister, Adze', to a Yellowknife park to play.There, we ran into some men and women who had participated in this research.They were delighted to see both of the children and wanted to talk with them, touch their hair, sing to them.They spoke loudly, and their eyes had tears in them as they talked to my niece and nephew.In Sana'a's eyes, however, I could see that, to him, these were friendly people who wanted to pay him some attention.He soaked it up.There was no judgment, no fear based on appearances.He had yet to learn those responses.He saw those excited men and women as people.It is my hope that this book generates the same spirit of recognition.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.316 | 0.185 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".