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Record W3140553322

Proceeding Report of the 2018/19 Symons Seminar Series

2019· article· en· W3140553322 on OpenAlexaff
Kaitlyn Mowat, Kirsten Solmundson, Michelle Arentsen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicCatholicism and Religious Studies
Canadian institutionsTrent University
Fundersnot available
KeywordsHonourThe artsStipendCertificateLibrary scienceMedia studiesPsychologySociologyVisual artsHistoryArtComputer sciencePolitical science
DOInot available

Abstract

fetched live from OpenAlex

The series is named in honour of the founding president of Trent, Professor Thomas H.B. Symons.  Professor Symons is an obvious choice to represent this series because of his dedication to higher education and student development, as well as his keen interest in graduate students and their research aspirations.  Graduate research at Trent University encompasses an array of topics across the arts and sciences.  This seminar series is a unique opportunity for graduate students from across disciplines to come together and share research and ideas. Six evening events are held annually at Catharine Parr Traill College from November to April.  Each month, two student speakers (one from the Arts/Humanities, and one from the Sciences) give twenty-minute presentations on their research. Questions and discussion follow each speaker, as well as an intermission for refreshments and socializing.  Abstracts are solicited twice during the academic year, usually in October and January, and they are reviewed by a team of volunteers to select the series speakers. Based on criteria of high quality research and ability to communicate to a broad audience, a panel of student judges selects the top speakers in the Arts/Humanities and the Sciences at the end of the year. These students are invited to present their lectures again at the Catharine Parr Traill College Symons Seminar Series Gala, typically held in April, and are awarded a Symons Seminar Series certificate and a stipend.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.701
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.209
Teacher spread0.194 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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