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Record W2992079797

Recent Experience With Acoustical Privacy Considerations in Academic Settings

2019· article· en· W2992079797 on OpenAlexvenueno aff
Mandy Chan, William J. Gastmeier

Bibliographic record

VenueCanadian acoustics · 2019
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsConfidentialityInternet privacyLegislationDistractionFeelingComputer sciencePsychologyComputer securityPublic relationsPolitical scienceSocial psychologyLaw
DOInot available

Abstract

fetched live from OpenAlex

Over the last number of years, the provision of increased levels of acoustical privacy and confidentiality has become an ever more important consideration in the design and functionality of office spaces and interview rooms. Occupants have always desired freedom from distraction and an enhanced feeling of privacy in their workplaces and offices. More recently, with the advent of privacy of information legislation, confidential levels of privacy are commonly required for spoken conversations. In academic settings, such spaces involve exam rooms for special needs students, mental health offices in student services facilities, cooperative education and other interview rooms and registration offices where sensitive information may be discussed. This article discusses several investigations related to reportedly poor levels of acoustical privacy in academic settings with regard to applicable criteria, suitable acoustical measurements and analysis and contributing factors.

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)0.009
metaresearch head score (Gemma)0.028
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0090.005
Scholarly communication0.0050.002
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.042
GPT teacher head0.371
Teacher spread0.329 · 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
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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