Bibliographic record
Abstract
Research Article| May 01 2018 Teaching Linguistics through Lexicography Mark Canada Mark Canada Indiana University Kokomo mark canada is professor of English and executive vice chancellor for academic affairs at Indiana University Kokomo. He is the author, coauthor, or editor of four books, including Introduction to Information Literacy for Students (Wiley, 2017) and Literature and Journalism in Antebellum America (Palgrave Macmillan, 2011). His articles on student success, Theodore Dreiser, Edgar Allan Poe, and other topics have appeared in The Chronicle of Higher Education, The Conversation, American Literary Realism, and other publications. E-mail: canadam@iuk.edu. Search for other works by this author on: This Site Google American Speech (2018) 93 (2): 311–323. https://doi.org/10.1215/00031283-6926179 Cite Icon Cite Share Icon Share Facebook Twitter Email Permissions Search Site Citation Mark Canada; Teaching Linguistics through Lexicography. American Speech 1 May 2018; 93 (2): 311–323. doi: https://doi.org/10.1215/00031283-6926179 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter Books & JournalsAll JournalsAmerican Dialect SocietyAmerican Speech Search Advanced Search The text of this article is only available as a PDF. Copyright © 2018 American Dialect Society2018 Article PDF first page preview Close Modal You do not currently have access to this content.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.171 | 0.055 |
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".