City stories: Publishing alternative dialogues from Vancouver’s past
Bibliographic record
Abstract
This project report presents a case study of Blood, Sweat, and Fear, authored by Eve Lazarus and published by Arsenal Pulp Press, to provide an example of an independent book publisher that leverages unconventional venues for book launch events for non-fiction titles chronicling alternative regional histories.The report begins with an introduction to Arsenal Pulp Press's history and mandate, and then moves into an overview of Lazarus's publishing history and network connections as a member of the Belshaw Gang.From there, the editorial and production components of Blood, Sweat and Fear will be discussed, with attention toward the challenges Lazarus faced when researching and writing her book.Finally, this report will outline the primary details of the book launch and marketing efforts, closing with an explanation of the pivotal role Arsenal Pulp Press plays in helping to preserve and reproduce lesser-known narratives about Vancouver in a tangible form.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.054 | 0.020 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".