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 The series has included volumes of previously unpublished work, or work in progress, by linguists with an ongoing affiliation with the Department, as well as volumes of papers from NWAV and the Penn Linguistics Conference. This volume contains selected papers from New Ways of Analyzing Variation 44 (NWAV 44), held October 22–25, 2015 at the University of Toronto. Thanks go to Luke Adamson, Spencer Caplan, Andrea Ceolin, Nattanun Chanchaochai, Sunghye Cho, Ava Creemers, Aletheia Cui, Sabriya Fisher, Amy Goodwin Davies, Duna Gylfadóttir, Ava Irani, Jordan Kodner, Wei Lai, Caitlin Richter, Milena Šereikaitė, Jia Tian, Lacey Arnold Wade, Robert Wilder and Hong Zhang for their help in editing this volume. Since 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. An example is below: Abtathian, Maya R., Abigail C. Cohn and Thomas Pepinsky. 2016. Methods for Modeling Social Factors in Language Shift. U. Penn Working Papers in Linguistics 22.2: Selected Papers from NWAV44, ed. H. Jeoung, 1-10. http://repository.upenn.edu/pwpl/vol22/iss2/2/. Publication 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. The PWPL editors can be contacted at: U. Penn Working Papers in Linguistics Department of Linguistics University of Pennsylvania Philadelphia, PA 19104–6228 working-papers@ling.upenn.edu http://ling.upenn.edu/papers/pwpl.html Helen Jeoung, Issue Editor Recommended Citation Jeoung, Helen. 2016. “Preface.” University of Pennsylvania Working Papers in Linguistics Vol. 22, Iss. 2, Art. 1. Available at: http://repository.upenn.edu/pwpl/vol22/iss2/1/.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.053 |
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; both teacher heads agree on what is shown here.
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".