How to Write a Scholarly Book Review for Publication in a Peer-Reviewed Journal: A Review of the Literature
Bibliographic record
Abstract
PURPOSE: To describe and discuss the processes used to write scholarly book reviews for publication in peer-reviewed journals and to provide a recommended strategy and book appraisal worksheet to use when conducting book reviews. METHODS: A literature search of MEDLINE, EMBASE, CINAHL, and the Index to Chiropractic Literature was conducted in June 2009 using a combination of controlled vocabulary and truncated text words to capture articles relevant to writing scholarly book reviews for publication in peer-reviewed journals. RESULTS: The initial search identified 839 citations. Following the removal of duplicates and the application of selection criteria, a total of 78 articles were included in this review including narrative commentaries (n = 26), editorials or journal announcements (n = 25), original research (n = 18), and journal correspondence pieces (n = 9). DISCUSSION: Recommendations for planning and writing an objective and quality book review are presented based on the evidence gleaned from the articles reviewed and from the authors' experiences. A worksheet for conducting a book review is provided. CONCLUSIONS: The scholarly book review serves many purposes and has the potential to be an influential literary form. The process of publishing a successful scholarly book review requires the reviewer to appreciate the book review publication process and to be aware of the skills and strategies involved in writing a successful review.
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 | 0.034 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".