Bibliographic record
Abstract
The University of Pennsylvania Working Papers in Linguistics (PWPL) is an occasional series published by the Penn Graduate Linguistics Society. The series has included volumes of previously unpublished work, or work in progress, by linguists with an ongoing affiliation with the Department, as well as volumes of papers from NWAV and the Penn Linguistics Colloquium/Conference. This volume contains selected papers from New Ways of Analyzing Variation 47 (NWAV 47), held October 18-21, 2018 in New York City, NY, at NYU.\nThanks go to Johanna Benz, Spencer Caplan, Gwen Hildebrandt, Jordan Kodner, Aini Li, Daoxin Li, Hassan Munshi, Lefteris Paparounas, Nari Rhee, Caitlin Richter, Jia Tian and Hong Zhang for their help in editing.\nSince Vol. 14.2, PWPL has been an internet-only publication. As of September 2014, the entire back catalog has been digitized and made available on ScholarlyCommons@Penn. Please continue citing PWPL papers or issues as you would a print journal article, though you may also provide the URL of the manuscript.\nAn example is below:\nHall, Erin and Ruth Maddeaux. 2020. /u/-fronting and /æ/-raising in Toronto Families. In University of Pennsylvania Working Papers in Linguistics 25.2, ed. Ruaridh Purse and Yosiane White, 51-60. Available at: http://repository.upenn.edu/pwpl/vol25/iss2/7\nPublication in the University of Pennsylvania Working Papers in Linguistics (PWPL) does not preclude submission of papers elsewhere; copyright is retained by the author(s) of individual papers.\nThe PWPL editors can be contacted at: U. Penn Working Papers in Linguistics, Department of Linguistics, 3401-C Walnut Street, Suite 300, C Wing, Philadelphia, PA 19104-6228 and working-papers@ling.upenn.edu.\nRuaridh Purse and Yosiane White, Issue Editors
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 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.559 | 0.417 |
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".