A Deeper Dive into the Cookbook Buyer: An Analysis of BookNet Canada Data and the Cookbook Industry
Bibliographic record
Abstract
Publishers rely on accurate sales data to make informed decisions about the books they publish, but how useful can that data be when the reporting systems that create it are incomplete? This report takes a granular look at the Canadian cookbook industry through the sales reporting and consumer surveys provided by BookNet Canada to see how accurately those systems reflect the reality of cookbook sales in Canada. Cookbooks are one of many specialty genres in the publishing spectrum that have unique sales channel distributions, which makes it difficult to make sweeping generalizations about their consumers. By transposing information from BookNet Canada’s SalesData and Deep Dive reports with Penguin Random House’s internal data to illuminate discrepancies, this report provides a more holistic snapshot of the genre and its consumers. It is a direct response to a 2016 report from BookNet Canada called The Deep Dive: The Cookbook Buyer.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| 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".