Bibliographic record
Abstract
Reviewed by: God Loves Hair by Vivek Shraya April Spisak Shraya, Vivek God Loves Hair; illus. by Juliana Neufeld. Arsenal Pulp, 2014 110 p Paper ed. ISBN 978-1-55152-543-3 $18.95 E-book ISBN 978-1-55152-544-0 $8.96 R Gr. 11 up In this reprint of a self-published Canadian book from 2011, a man looks back on his youth with wit, grief, anger, and a keen eye about how he became the adult he now is. As a kid, the author was an Indo-Canadian boy negotiating efforts at fitting in even with all the ways he clearly did not, from his long hair to his interest in things that have traditionally signaled girlhood. Some passages are short, describing a particular incident, while longer essays summarize eras of his childhood and adolescence. All sections are titled and accompanied by a single illustration. There is clear collaborative effort between the author and the illustrator, Neufeld, and the drawings are indeed one of the strongest elements, sharpening what are occasionally long passages into one searing image that captures the same tone. For the most part, however, Shraya has edited this carefully, using each multi-page story to add to the ones before, resulting in a volume that is clearly written by retrospective adult, but one who still carries the scars of his childhood and adolescence with him. This may resonate most with older teens who may have also seen enough glimpses of life beyond high school to fully appreciate the evolution of the trapped, bullied kid represented to the sharp, poetic man who remembers being him. [End Page 229] Copyright © 2014 The Board of Trustees of the University of Illinois
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".