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Record W7135281453

Preface

2016· report· W7135281453 on OpenAlexaboutno aff
Helen Jeoung

Bibliographic record

VenueScholarlyCommons (University of Pennsylvania) · 2016
Typereport
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsApplied linguisticsStyle (visual arts)Computational linguisticsDoctoral dissertation
DOInot available

Abstract

fetched live from OpenAlex

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 The series has included volumes of previously unpublished work, or work in progress, by linguists with an ongoing affiliation with the Department, as well as volumes of papers from NWAV and the Penn Linguistics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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.463
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0030.001
Scholarly communication0.0060.005
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.5370.404

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.

Opus teacher head0.044
GPT teacher head0.248
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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