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Record W2272398291

Communicating Functions and Impacts to Urban Communities: An Ontology for the Infrastructure Construction Industry

2012· article· en· W2272398291 on OpenAlexaff
Sherif Kinawy, Tamer E. El-Diraby

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOntologyComputer scienceKnowledge managementRepresentation (politics)Semantic WebContext (archaeology)Process (computing)Data scienceWorld Wide WebPolitics
DOInot available

Abstract

fetched live from OpenAlex

Meeting the community’s needs requires a focus on communicating project functions as the key to enhancing their participation in infrastructure planning and design. Earlier work has established that the representation of knowledge through ontologies can be an effective means of employing social and semantic web technologies as a support platform for community engagement. This research builds on the aforementioned proposal for a semantic community empowerment framework. The current research revisits the representation of product function in the context of construction projects. The resulting representation aims to overcome the limitations of the traditional representation by augmenting economic, cultural, environmental and political dimensions to functions and impacts which are portrayed as complex entities. These dimensions involve a representation of non-tangible and often non-measurable components of utility to which members of the public can relate; hence enhancing threeway communication as part of the participation process. In this paper, the ontology design is documented through a demonstration of a number of concepts: construction products, their functions and impacts, and communication channels. Furthermore, the main services of the Application Programming Interface (API) are discussed as a standard method for exposing the ontology to third-party software developers. Finally, a sample application is proposed as a validation of this semantic framework.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0040.009
Scholarly communication0.0060.009
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.274
Teacher spread0.233 · 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 designTheoretical or conceptual
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
Published2012
Admission routes1
Has abstractyes

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