Bibliographic record
Abstract
Denman, K.L. Agent Angus. Victoria: Orca Book Publishers, 2012. Print. An Orca Currents book for reluctant middle school readers, the story follows Angus and his best friend Shahid on their mission to solve a mysterious theft. Canadian author K.L. Denman writes in her usual first-person narrative style with characteristic elements of mystery, science and romance. The story takes place at a school where a stink bomb incident has led to all the students gathering on the front lawn. Right from the book’s introduction (“I’m not a lucky guy. Today luck has chosen to place me next to the one and only Ella Eckles”), readers are taken inside Angus’ head and will be rooting for him along the way. When his crush’s cherished sketchbook goes missing, Angus poses as a mentalist who can solve the crime by reading people. He ‘proves’ his abilities to Ella by pointing out the shifty stink bomb perpetrator right before he is nabbed by the principal. Humorous elements run throughout such as when the boys consider various spy devices (Gordon the ‘too obvious’ robot, a rocket pack launched from a plane, pricey video cameras hidden in smiley face buttons or baseball hats which are not allowed in school, and affordable but oversized rear view sunglasses). Suspense builds as the various suspects are considered. Is the thief their fellow classmate, their art teacher or someone they least suspected? And what could their motive be? This quick read full of spying and intrigue will have readers flipping pages to solve the mystery of the sketchbook and find out if Angus will finally confront the truth. The fluid writing style with varying sentence lengths adds to the drama and pace of the story. This light-hearted story makes a great choice for reluctant readers but lacks deep meaning. It may not appeal to readers who are looking to be challenged. Those looking for a light, easy read will find it enjoyable.Recommended: 3 out of 4 stars Reviewer: Lori Williams Lori Williams has been teaching at Forest Grove School in British Columbia for the past 6 years and feels lucky to be part of a wonderful team of colleagues and students. This year she is teaching grade 5 at Forest Grove and is also a graduate student in the University of Alberta’s Teacher-Librarianship by Distance Learning program.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.116 | 0.105 |
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".