MétaCan
Menu
Back to cohort
Record W4238719859 · doi:10.1504/ijbeam.2016.080556

Choosing between modern and heritage buildings for professional services

2016· article· en· W4238719859 on OpenAlexaff
Lindsay J. McCunn, Robert Gifford

Bibliographic record

VenueInternational Journal of the Built Environment and Asset Management · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBusinessQuality (philosophy)Service (business)Financial servicesMarketingPublic relationsService providerFinancePolitical science

Abstract

fetched live from OpenAlex

The role of building façades in the consumer choice process is not well understood but arguably influences consumers' first impressions of a business and the level of service provided by the professional working within. In two studies, photographs representing exteriors of two building types (purpose-built office and converted heritage house) were shown to participants who chose between them for hypothetical dental, legal, financial, and medical services and assessed them for expected comfort and quality of service. In Study 1, students preferred office buildings for all services. In Study 2, community residents also preferred office buildings for dental, financial, and medical services but not for legal services. In both studies, participants had more experience with office buildings compared to heritage houses for all four services and anticipated more comfort and service quality in offices, especially for dental, financial, and medical services compared legal services. However, more comfort was expected by those in Study 2 when considering legal services over the other three services in heritage houses. Although office buildings are commonly experienced and preferred as settings of professional practice, heritage houses can be legible as places to access comfortable, quality service.

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 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 score0.811
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.244
Teacher spread0.205 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of the Built Environment and Asset ManagementSame topicCultural Heritage Management and PreservationFrench-language works237,207