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Record W2087614422 · doi:10.1121/1.4806235

Acoustical characteristics of Technology Educational Shops in British Columbia

2013· article· en· W2087614422 on OpenAlexaffabout
Ahmed Summan, Murray Hodgson

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsUniversity of British Columbia Hospital
Fundersnot available
KeywordsReverberationIntelligibility (philosophy)AcousticsComputer sciencePhysics

Abstract

fetched live from OpenAlex

Technology Educational Shops (TES) are designed to develop high school students’ technological literacy. Their acoustical conditions play a dominant role in the quality of these environments. TES are, at the same time, classrooms for learning and industrial workshops for making things. Each use has its own standards governing its acoustical characteristics: ANSI S12.60-2002 for classrooms and the Ondet & Sueur DL2 criteria for workshops. A major conflict could exist by using the same room for two different purposes. This study investigated this conflict by evaluating the acoustical characteristics of 20 unoccupied wood, metal and automotive shops. It conducted measurements of background noise level (BNL), reverberation time (RT), speech intelligibility index (SII), and DL2. Results showed that BNLs and RTs in most TES were higher than the acceptability criteria for unoccupied core learning spaces. SII values indicated bad/poor speech intelligibility for normal and raised voice levels and reasonable/good speech intelligibility for loud and shout voice levels. DL2 values were found acceptable in TES larger than 100 m2 in floor area. In general, these results indicate the poor acoustical conditions of TES as classrooms, and the need for special sound control measures.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.276
Teacher spread0.267 · 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 designObservational
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
Published2013
Admission routes2
Has abstractyes

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