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Record W4404586916 · doi:10.3992/jgb.19.4.263

USING THE LIBRARY FOR RESEARCH IN THE BUILDING SCIENCES

2024· article· en· W4404586916 on OpenAlexaff
Cathryn Copper

Bibliographic record

VenueJournal of Green Building · 2024
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsArchitectural engineeringEngineeringConstruction engineeringComputer science

Abstract

fetched live from OpenAlex

ABSTRACT In your research and daily life, you may have primarily relied on Google as your primary search engine. This general search tool may have successfully fulfilled your information needs thus far. However, as you advance through your studies, you reach a stage in your education at which you are expected to find and report on substantive, scholarly research conducted by professionals in a field, and Google no longer suffices. The sources for this type of scholarly information are peer-reviewed journals, books published by academic and university presses, and formal reports issued by government agencies. But these are not the type of sources that are accessed in a basic Google search. Therefore, search engines like Google are usually inadequate for the tasks you will be expected to accomplish. So, if you can't “just Google it,” how do you conduct research at this new level? One answer is the library!

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)0.007
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: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.303
GPT teacher head0.490
Teacher spread0.186 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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