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Record W4220768782 · doi:10.1061/9780784483954.021

Online Engagement of Facility Users in Rehabilitation Decision Making

2022· article· en· W4220768782 on OpenAlex
Ahmed Attalla, Tamer E. El-Diraby

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueConstruction Research Congress 2022 · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsHudbay Minerals (Canada)University of Toronto
Fundersnot available
KeywordsProcess (computing)Computer scienceExploratory researchKnowledge managementProcess managementBusiness

Abstract

fetched live from OpenAlex

This paper outlines an exploratory case study implemented in a Canadian high school for engaging end-users in the re-design of their facilities to achieve the practical needs and comfort levels of users while also moving toward sustainable energy efficient building practices. The case study aimed to use interactive online systems to help facility users in sharing suggestions about possible changes to their building and empower them to take the lead in the re-design of their building. The observed outcomes are mixed. Facility users welcomed the interactive nature of the online tool. They offered suggestions for changes and improvements unique to their building and demonstrated an ability to overcome technical knowledge shortcomings. The discussions and interactions between participants appeared limited until participants were divided into smaller groups. Feedback from participants suggests that the overall process and online tool were well received. However, the general level of participation was lower than expected.

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.

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)0.006
metaresearch head score (Gemma)0.002
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 score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.073
GPT teacher head0.355
Teacher spread0.282 · 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