Excavating the slush pile at McClelland & Stewart
Bibliographic record
Abstract
Every book publisher in Canada receives unsolicited submissions from writers hoping to be published but who lack an agent or a connection within the house. These submissions are often referred to as the "slush pile." About fifteen years ago, before literary agents rose to such importance in Canadian book publishing, the quality of unsolicited submissions was much higher. In an agented environment, when firms rarely acquire from the slush pile, the quality of slushpile submissions has diminished. This report analyzes the McClelland & Stewart slush pile. It outlines the ways in which McClelland & Stewart acquires manuscripts, the kinds of submissions the company receives in its slush pile, how it responds to them, and why the company continues to evaluate these proposals. It finds that, with very rare exceptions, the submissions writers send unsolicited to the company are either in genres the company does not publish, are written at a level the company deems unacceptable for publication, or otherwise do not fit the M&S publishing mandate. However, the report concludes that the company should continue to evaluate unsolicited submissions, as a way to train junior editors, to maintain openness to the writing community, and to give unagented writers a forum in which their work can be assessed.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.010 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".