Bibliographic record
Abstract
Theobald, Joeseph and Kévin Viala. Martin est en colère. Toronto: Éditions Scholastic, 2014. Print.This picture book tells a humorous story about a sheep that gets angry very easily when he does not get what he wants. His anger turns him into a nasty and ferocious monster. In the end, he becomes stranded and realizes that it’s not so fun to get mad.I really enjoyed this silly and funny book. I found the pictures to be amusing, colourful and very expressive. The visuals were large and really demonstrated what was happening in the book, to the point where words were not necessarily required to understand the story. The pictures made the story funny, especially when the main character becomes a monster.I loved the fact that all the characters in this book were animals. Animal books are often a great fit for younger students since the character is more easily relatable and does not imply any cultural or ethnic barriers.When it came to the writing, the author used language that was not too complicated or wordy. I especially like this for those students in French Immersion programs since the vocabulary is not too advanced for native English speakers learning French. The text and the images make this a great read for younger students at an intermediate level in the French language.What struck me the most about this book is the open ending. This ending leaves the reader wondering if Martin will make a change in his action this time or whether he will fall back on old habits. Overall this was a feel good, easy to understand and humorous picture book that displays a great message about looking for solutions instead of losing your cool.Highly recommended: 4 out of 4 starsReviewer: Vanessa PesantVanessa Pesant is a grade 2 French immersion teacher in Beaumont. She is currently working on completing her master's degree in Elementary Education.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".