Video Editorial: Anatomy of a Book Review: Why We Write, Why We Read Video
Bibliographic record
Abstract
Book reviews have long been a staple of the scholarly conversation. Reviews connect researchers to recent publications, expand the audience for individual titles, and shape the boundaries of what counts as part of the library and information studies literature. At the same time, reviews can reinforce traditional academic hierarchies, particularly in terms of what books are reviewed and who is invited to render their opinion. Outgoing C&RL book review editor Emily Drabinski and a group of recent reviewers discussed the state of the academic book review in this lively conversation from January 20, 2023. Emily Drabinski, City University of New York Kaia MacLeod, University of Calgary Mallary Rawls, Florida State University Ashley Roach-Freiman, University of Memphis Charlotte Roh, California Digital Library Lynne Stahl, Wesleyan University Darren Sweeper, Montclair State University Anders Tobiason, Boise State University View the recording of the Anatomy of a Book Review: Why We Write, Why We Read webcast on the ACRL YouTube channel at https://youtu.be/iCIAEFPNkrM .
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.136 | 0.099 |
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".