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Record W7036516210

Beloit College Vice President Named Dean of the Faculty at Lawrence University

2005· article· en· W7036516210 on OpenAlexaboutno aff

Bibliographic record

VenueLux Scholarship And Creativity At Lawrence University (Lawrence University) · 2005
Typearticle
Languageen
FieldMedicine
TopicHeart rate and cardiovascular health
Canadian institutionsnot available
Fundersnot available
KeywordsVice presidentLiberal arts educationAdministration (probate law)State (computer science)The artsColumbia universityHigher education
DOInot available

Abstract

fetched live from OpenAlex

Lawrence University President Jill Beck has announced the appointment of David Burrows as provost and dean of the faculty at the college. Burrows, currently the dean of the college and vice president for academic affairs at Beloit College, will begin his duties at Lawrence July 1, 2005. He replaces Kathleen Murray, who has served as dean since June 2003. Murray, former dean of the Lawrence Conservatory of Music and professor of piano, has been named provost of Birmingham-Southern College in Alabama. “Dr. Burrows will work with the faculty and with me on all matters relating to the continued advancement of Lawrence,” said Beck in announcing the appointment. “He possesses a depth of experience in liberal arts administration that equips him to succeed with distinction in the coming years.” A cognitive psychologist, Burrows has served as dean at Beloit since 1997. A native of New York City, he spent eight years on the faculty at the State University of New York at Brockport and 17 years at Skidmore College, including three as associate dean of the faculty there. He earned a bachelor’s degree in psychology from Columbia University and a master’s degree and Ph.D. in psychology from the University of Toronto.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.149
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.1490.046

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 head0.025
GPT teacher head0.239
Teacher spread0.215 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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