Bibliographic record
Abstract
Readers for Style 2017–2018 John V. Knapp, Editor This page(s) is an acknowledgement for the help and expertise of those named below who have volunteered their time and their wisdom to review manuscripts for Style. Without the help of these talented and generous scholars, our whole enterprise of literary scholarship and criticism would soon disappear. We at Style extend our thanks and reiterate our feelings of gratitude for all who helped by reviewing this past year. Many thanks to you all, and if I have inadvertently missed your name, or have listed your institutional address as different from the one you now hold, please let me know ASAP and I will correct the record quickly. Jan Alber, RWTH Aachen University, Germany William Baker, Northern Illinois University Betty Birner, Northern Illinois University Brian Boyd, University of Auckland, NZ Edward Callary, Northern Illinois University Marco Caracciolo, Ghent University, Belgium Joe Carroll, St. Louis University Alison Case, Williams College Athanasia Chalari, University of Northampton, UK Timothy Crowley, Northern Illinois University Richard Cureton, University of Michigan Roger Dalrymple, Oxford Brookes University, UK Maire Doyle, University College, Dublin, Ireland Jeffrey Einboden, Northern Illinois University Philip Eubanks, Northern Illinois University Sibelan Forrester, Swarthmore College Helena Goscilo, The Ohio State University Marlene Goldman, University of Toronto, Canada Marina Grishakova, University of Tartu, Estonia Darryl Hattenhauer, Arizona State University Mari Hatavara, University of Tampere, Finland David Hoover, New York University Emma Kafalenos, Washington University in St. Louis Suzanne Keen, Hamilton College Jacob Lothe, University of Oslo, Norway Maria Mäkelä, University of Tampere, Finland Thomas McCann, Northern Illinois University Christopher McGunnigle, University of Louisiana at Lafayette [End Page 517] Carla Mulford, The Ohio State University Henrik Skov Nielsen, Aarhus University, Denmark Ning Yizhong, Language and Culture University, Beijing, PR China Thomas Pavel, University of Chicago Vincent Pecora, University of Utah Jenny Penberthy, Capilano College, Vancouver, Canada Gerald Prince, University of Pennsylvania Karen J. Renner, Northern Arizona University Marie-Laure Ryan, Independent Scholar, Colorado Tina Steiner, Stellenbosch University, South Africa Shen Dan, University of Beijing, PR China Roi Tartakovsky, Tel Aviv University, Israel Reuven Tsur, Hebrew University, Israel Richard Walsh, University of York, UK Ann Willey, University of Louisville [End Page 518] Copyright © 2018 The Pennsylvania State University
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.764 | 0.816 |
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".