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Record W7046900324

EDRA Archives donated by John Zeisel and Jacqueline Vischer

2016· article· en· W7046900324 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Commons - Andrews University (Andrews University) · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
Fundersnot available
KeywordsWorkspaceWork (physics)Field (mathematics)Quality (philosophy)
DOInot available

Abstract

fetched live from OpenAlex

Boxes #21-24 -- Books Dr. Jacqueline Vischer is an Environmental Psychologist specializing in environments for work. She is a founder of the field known as workspace psychology. She has published several books, including Environmental Quality In Offices (1989), Workspace Strategies: Environment As A Tool For Work (1996), L’Évaluation des environnements de travail : la méthode diagnostique (co-author Gustave-Nicolas Fischer, 1998), and Space Meets Status: Designing Workplace Performance (2005). In addition, Vischer has co-edited two books with Wolfgang Preiser. She speaks at trade shows and conferences throughout North America and in Europe, Asia and Australia, and she has contributed numerous chapters to volumes on facilities management, building performance, workplace psychology and building programming and evaluation. As expert consultant, Vischer has advised a wide range of organizations internationally on managing workspace comfort, designing innovative workspace, and planning workspace change. She is Professor Emeritus at the University of Montreal, where she successfully ran the Interior Design program and founded the New Work Environments Research Group (Groupe de recherche sur les environnements de travail). Many of her writings are available at – and can be downloaded from – www.jacquelinevischerbiu.com

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.008
GPT teacher head0.180
Teacher spread0.172 · 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.

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
Published2016
Admission routes1
Has abstractyes

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