Book Review: <i>Unspinning the Spin: The Women’s Media Center Guide to Fair and Accurate Language</i> , by Rosalie Maggio
Bibliographic record
Abstract
Unspinning the Spin: The Women's Media Center Guide to Fair and Accurate Language. Rosalie Maggio. Washington, DC: WMC Press, 2014. 543 pp. $30 pbk. $14.99 ebk.To help everyone understand and be understood is the ambitious goal of the first publication from WMC Press, Unspinning the Spin: The Women's Media Center Guide to Fair and Accurate Language, by Rosalie Maggio. The preface is written by Gloria Steinem and Robin More, former Ms. magazine editors and fellow co-founders (along with Jane Fonda) of the Women's Media Center, which was started in 2005 with a mandate to make women visible and powerful in the media.Rosalie Maggio is an established author of writing and how-to guides, including The Bias-Free Word Finder: A Dictionary of Nondiscriminatory Language and How to Say It: Choice Words, Phrases, Sentences & Paragraphs. Unspinning the Spin is meant to be more than a guidebook, however. In addition to serving as a primer and thesaurus of inclusive language, it also provides encyclopedia style capsules delving into the history of both biased words and phrases, and of important events and concepts in the ongoing struggles for gender, race, and class equality in the United States.The promotional literature markets the book to both media producers (journalists, bloggers, students, communications professionals, et al.) and the consumers of that media. Unspinning the Spin draws from and brings together in one volume materials from issue-specific writing guides, including The Handbook of Nonsexist Writing (Miller & Swift), Uprooting Racism: How White People Can Work for Racial Justice (Kival), Weasel Words: The Dictionary of American Doublespeak (Wasserman & Hausrath), and The AP Stylebook.This book is most aptly described as a compendium, with the wide range, eclectic content, and uneven degree of detail that term implies. The entries range from succinct guidelines, such as broad (woman) no and Jew boy completely offensive to 500+ word essays on topics such as political spectrum, disabilities, and torture. The content is arranged alphabetically, with cross-referencing to related terms.The how-to nature of this publication is clear from the get go, as the introduction does double-duty as a writing guide. Maggio here, for example, advises against using gendered suffixes, like those in waitress and heroine, arguing that such endings perpetuate the notion that the male form is the norm, the female a subset. She explains how and why to avoid pseudo-generics such as he and mankind. The Introduction also includes definitions of key terms, like bias, gender, and sex. It ends with a cogent discussion on the ephemeral nature of language and with convincing rejoinders to those who object to bias-free and inclusive language as unimportant, non-traditional, and/or impossible to keep up with. …
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.201 | 0.185 |
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".