Bibliographic record
Abstract
Spires, Ashley. Larf. Toronto: Kids Can Press, 2012. Print. This book is a twist on the “lonely hearts” theme, with lots of humour. Larf is a sasquatch who feels “like nobody knows [he] even exist[s]. Author Ashley Spires has given him a lifestyle that somewhat mirrors her own – jogging, gardening, and walking his pet bunny, Eric. Larf is excited to hear that another sasquatch is going to make an appearance in a town nearby, so he decides to go meet him. When he gets there, he is disappointed to find out that it is a guy in a sasquatch suit who tells him that “Sasquatches aren’t real”. But he does meet another sasquatch, who has also been enticed into town by the sasquatch performance, a girl sasquatch named Shurl. She carries a handbag and wears a pink dress. And so a friendship begins. This story is of the same genera as E.T., Shrek and Monsters, Inc. that make scary, imaginary creatures less scary by making them look cuddly or childlike and showing them leading day-to-day lives similar to our own. Larf is both cuddly, looking a bit like a furry teddy bear and child-like in that his head is large for his body, like a baby’s. Larf does ordinary things. He eats at a table, rides a bus, does laundry on Wednesday and would like to have a friend to play teeter-totter with him. It is hard to be afraid of a furry creature who is lonely and wants to have friends. The drawings are all cartoon-like and presented in a variety of attractive ways. Some fill more than a page while others are overlapped like photographs. Sometimes there are four small panels on a page, each with its own caption. There are a few jokes in the book. For example, we are told that Larf “is a master of camouflage”, but even small children will see that his beret, scarf and grey pants are not enough to disguise a giant-sized sasquatch. This book is fun to read and would be an excellent for public and school libraries, particularly those in “sasquatch country”, where children might have been spooked by scary stories about sasquatches. Highly Recommended: 4 stars out of 4 Reviewer: Sandy Campbell Sandy is a Health Sciences Librarian at the University of Alberta, who has written hundreds of book reviews across many disciplines. Sandy thinks that sharing books with children is one of the greatest gifts anyone can give.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.564 | 0.536 |
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".