Print book formats: A closer look at how Simon & Schuster Canada uses formats to find their readers
Bibliographic record
Abstract
When we read a book, we rarely give much thought to the format in which that book is published. But had the book not presented itself to us in a format that suited our taste and our pockets, we would probably not have picked it up. That’s basically what publishers do: they strive to position their books in the right form and with the right price so that they will find their intended readers. In the summer of 2018, during my professional placement at Simon & Schuster Canada, I learnt how format decisions impact the fate of books, and how the company made successes of certain books by changing the format from hardcover to trade paperback. This report is a culmination of my observations and learnings and is based on interviews I conducted with key members of the staff and data and information provided by Simon & Schuster Canada. The subject of print book formats is complex. My hope is that this report throws light on the state of formats as it stands today in Canada.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.007 |
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".