What They Didn’t Teach You in Graduate School: 199 Helpful Hints for Success in Your Academic Career (review)
Bibliographic record
Abstract
Reviewed by: What They Didn’t Teach You in Graduate School: 199 Helpful Hints for Success in Your Academic Career Steven E. Gump (bio) Paul Gray and David E. Drew. What They Didn’t Teach You in Graduate School: 199 Helpful Hints for Success in Your Academic Career. Sterling, VA: Stylus, 2008. Pp. xxiv, 147. Paper: ISBN-13 978-1-57922-264-2, US$15.95. More than twenty years ago, Linda Brodkey, a specialist in the teaching of writing, observed that ‘academics live their lives in print.’1 It should be no surprise, then, that in Paul Gray and David E. Drew’s book of helpful hints for academics, over one-third of the suggestions are related to writing or scholarly publishing. In fact, three of the six hints in the first chapter, ‘Basic Concepts,’ are directly related to scholarly publishing: ‘The number of papers required for tenure is N + 2, where N is the number you published’ (7); the ‘known people’ in any field are ‘people who write books,’ ‘people who publish papers,’ and ‘people active in their professional societies’ (7); and ‘every paper can be published somewhere’ (8). With this chapter, the authors set the stage to describe the realities of academic life – a world about which most graduate students [End Page 323] ‘have only the vaguest concepts’ (1), and in which writing and publishing can be as common and as important as teaching, research, and service. What They Didn’t Teach You in Graduate School: 199 Helpful Hints for Success in Your Academic Career is a frank and, at times, admittedly irreverent and cynical guide for new faculty members or graduate students anticipating careers in academe. The authors, a professor emeritus of information science (Gray) and a professor of education (Drew), both at Claremont Graduate University in California, offer insights and advice on such general topics as job hunting, teaching and service, research, tenure and academic rank, diversity in academe, collegiality and institutional citizenship, health, and writing and publishing. Recognizing that institutions of higher education in countries as close as Canada and Mexico may follow different academic assumptions and procedures (2), the authors limit the generalizability of their advice to institutions in the United States. Moreover, because the observations are based on the authors’ experiences, research institutions take centre stage – institutions where faculty members engage heavily in research and writing in addition to teaching and service, and where tenure and promotion are determined largely by the quality and quantity of faculty members’ scholarly output. Prefaced by two brief but thoughtful forewords and an introduction, this attractive volume, which includes sixteen amusing and apropos full-page cartoons by Matthew Henry Hall, presents its 199 hints in fifteen chapters and four appendices. Most hints are described in one or two paragraphs, though some include tables or more substantial examples and considerations. Chapters and appendices include anywhere from two to forty-three hints apiece (though the concluding chapter has none); the chapter on ‘Life as an Academic’ includes the most. That particular ideas appear in multiple hints in different sections of the book is a sign not of disorganization but of the ways in which certain overarching considerations affect academic life – and the ‘academic writing life,’ to borrow a concept from Chris Thaiss and Terry Myers Zawacki.2 Specifically, with respect to writing and publishing, the idea of publication as ‘portable wealth,’ the benefits of publishing while still in graduate school, the potential value of co-authorship, how to [End Page 324] become a ‘known’ scholar in a field through publishing and specializing, how academic writing is perceived by different types of readers (editors, reviewers, students, colleagues, members of grant committees), and why good writing is important are concepts that appear in numerous locations throughout the book. Several of these ideas appear in some form in a targeted chapter titled ‘On Publishing,’ which offers nineteen hints wherein the authors also laud the merits of refereed journals, excoriate vanity presses, and briefly explore some of the differences between trade publishers and scholarly presses. As the ‘prize’ for academics (45), tenure underlies several of Gray and Drew’s suggestions. Scholarly publishing is the key, as explained in Hint 40 (paradoxically included...
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".