Bibliographic record
Abstract
Battle, Craig and Ramón Pérez. Max Finder Mystery Collected Casebook, Vol. 6, 2012. Toronto: Owlkids Books. Series created by Liam O’Donnell. Print. Max Finder comics will be familiar to readers of OWL magazine, where the comic strip has been a regular feature. The strip, originally created by Liam O’Donnell, is anthologized in the series Max Finder Mystery: Collected Casebook. The sixth volume in this series features the work of writer, Craig Battle and illustrator, Rámon Pérez, and includes ten illustrated stories, two textual stories, tips for holding a Max Finder mystery party and advice for teachers. Each strip sets up a perplexing mystery that youngster Max Finder and his friends unravel with keen observations and intricate deductions. Readers must study the details in the story and illustrations in order to match wits with the sleuths. It’s a bit like Encyclopedia Brown meets Where’s Waldo. The puzzles are not easy to figure out, and it takes discipline not to flip ahead to the answer. The dedicated reader, though, learns to exercise powers of careful reading (and seeing) not normally required for tweets and texts. The stories feature: a diverse cast of heroes and villains that seems designed to appeal to girls and boys; memorable situations (e.g., two competing house parties on the same night); and elements of contemporary culture (e.g., gaming tournaments). Readers are led to consider motive and opportunity, and look for logical inconsistencies. The collection is a convenient format for Max Finder fans who really want to dig in and flex their brains. It could also be an introduction to OWL magazine for some. Recommended: 3 out of 4 starsReviewer: John HuckJohn Huck is a Metadata and Cataloguing Librarian at the University of Alberta. He holds an undergraduate degree in English literature and maintains a special interest in the spoken word. He is also a classical musician and has sung semi-professionally for many years.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.316 | 0.178 |
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".