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 The series has included volumes of previously unpublished work, or work in progress, by linguists with an ongoing affiliation with the Department, as well as volumes of papers from NWAV and the Penn Linguistics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. This volume contains selected papers from the 42nd NWAV Conference, held from October 17-20, 2013 at the University of Pittsburgh and Carnegie Mellon University in Pittsburgh.\nThanks go to Aletheia Cui, Amy Goodwin Davies, Sabriya Fisher, Helen Jeoung, Taylor Jones, Soohyun Kwon, Milena Šereikaitė, Einar Freyr Sigurðsson, Betsy Sneller, and Robert Wilder for 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. An example is below:\nBrook, Marisa. 2014. Comparative Complementizers in Canadian English: Insights from Early Fiction. In U. Penn Working Papers in Linguistics 20.2: Proceedings of NWAV 42, ed. D. Gylfadottir, 1-10. http://repository.upenn.edu/pwpl/vol20/iss2/2\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 619 Williams Hall, University of Pennsylvania Philadelphia, PA 19104–6305 working-papers@ling.upenn.edu http://ling.upenn.edu/papers/pwpl.html\nDuna GylfadottirIssue Editor
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.104 | 0.000 |
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 teacher head, 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".