A conversation with former editor-in-chief of Parets Magazine ans editor of On Four Walls
Bibliographic record
Abstract
I Wrote on All Four Walls: Teens Speak Out on Violence edited by Fran Fearnley is a disturbing yet truthful account of how teens, abused and bullied, somehow survive the brutality of their existence and manage to find meaning in their lives despite everything that happens to them. The book sponsored by the Toronto Public Library system is the first in a series of publications that seeks to give voice to the stories, thoughts and poems of teens living in the city of Toronto. Josephine Bryant, City Librarian, Toronto public Library writes, "here are the powerful stories of youth who agreed to share their experiences and their stories. It is an attempt to bring a positive approach to the issue of street youth. " Fran Fearnley says Adam, Allan, Caitlin, Claire, Debbie, Don, Janice, Kevin and Sue told us about their encounters with violence in one on one-taped interviews. Their very own words transcribed from those interviews, are what you will read. They are authentic oral testimonies as told to an empathetic listener. Dr. Fred Mathews a social worker with Central Toronto Youth Services concludes the book with advice to children and teens about what to do if they or others are or know of someone in need. He reminds young people "they must learn to trust teachers and educators and tell a teacher, principal, school worker, or other adult . I f they do not listen, tell a police officer, a nurse, parents, a friend but tell someone. Don't let previous negative experiences with some adults in power affect your decision, keep trying until you find someone who will believe you and take action. The important thing is do not stand by and let others be harmed. Violence is fueled by silence and stops when we all take a stand." (page 143)
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.072 | 0.035 |
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".