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Record W1990022978 · doi:10.1353/scp.0.0004

How to Write for a General Audience: A Guide for Academics Who Want to Share Their Knowledge with the World and Have Fun Doing It (review)

2008· article· en· W1990022978 on OpenAlexvenueno aff
Stephen K. Donovan

Bibliographic record

VenueJournal of Scholarly Publishing · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicPublishing and Scholarly Communication
Canadian institutionsnot available
Fundersnot available
KeywordsSalarySociologyAudience measurementMedia studiesPublic relationsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Reviewed by: How to Write for a General Audience: A Guide for Academics Who Want to Share Their Knowledge with the World and Have Fun Doing It Stephen K. Donovan (bio) Kathleen A. Kendall-Tackett . How to Write for a General Audience: A Guide for Academics Who Want to Share Their Knowledge with the World and Have Fun Doing It. Washington, DC: APA Life Tools, 2007. Pp. xiv, 286. Paper: ISBN-13 978-0-9792125-3-6, US$19.95. doi: 10.3138/jsp.39.3.318 Yet another self-help book for the academic author? How many of these books do we need? Ask any editor and he or she will probably say there aren't enough, or, at least, that the authors who need to read such books aren't doing so. Yet How to Write for a General Audience (HWGA) isn't quite the same as many of the others, in that it encourages academic authors to spread their wings and write for a broader audience, rather than the handful of experts in the field who usually read their papers. Nevertheless, HWGA includes many suggestions that are more generally applicable. But surely academics are paid a salary to enable them, at least for part of the time, to write for other academics; why write for anyone else? That is, most academics write for an informed, albeit limited, circle of scholarly specialists. HWGA encourages the academic to write for an interested, larger, and wider audience. How is writing for many different from writing for a select group? Certainly, the element of debate with a small circle of like-minded experts is removed; although a general audience may still be disputatious, it is also more likely to regard Professor A or Dr B as carrying tablets of stone. Yet a more general audience can be broad, but still informed; in my own field, magazines such as Geology Today are popular with amateurs, students, and professionals alike. Kathleen Kendall-Tackett has written a book of many parts. Some of her ideas were already well known to me, while other suggestions were new, but all are interesting and well presented; as a member of a general audience (a geologist), the author (a health psychologist) kept me enthralled. Her style is both informative and persuasive, not least where examples and quotations provide support for her key ideas and recommendations. The first eight chapters deal with general themes that apply equally to writing articles [End Page 318] and writing books. Kendall-Tackett's approach is instructive, informative, and full of ideas on good presentation of academic concepts. There follow five chapters on book proposals, contracts, and marketing, perhaps not so broadly relevant but a useful primer for anyone writing or editing a first book. Chapter 2, 'Finding Time to Write: Time Management for Writers,' has something for all of us. The wall between productive writing and procrastination is thin;1 writing takes time, so any writer must make time to write.2 I also support the idea (18) that a productive author reads widely.3 This is something that I do naturally, and it has dawned on me only recently that my habit of reading at least a book per week feeds back into my writing in many ways, from improving my vocabulary to highlighting ideas in other fields that are applicable to my own. One of my own ways of encouraging progress when writing is to count the number of words after every writing session and record the latest figure at the end of the text, along with the date and time. This indicates my progress (or otherwise) every time I sit down to write. Even on days when writing is almost crowded out of my schedule, grabbing fifteen minutes to tidy a paragraph of a paper in preparation (as I'm doing right now) enables me to progress on that project, however minimally. Kendall-Tackett includes a section in this chapter on writing as a parent. At least three times per week I get up at least two hours before the children do; this works for me, but it is good to see other potential solutions mentioned. Chapter 3, 'Why We...

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0050.006
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0370.061

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.

Opus teacher head0.103
GPT teacher head0.298
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2008
Admission routes1
Has abstractyes

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