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A NEW ONTOLOGICAL PERSPECTIVE FOR INTEGRATION OF SOCIAL AND PHYSICAL ENVIRONMENTS: DISABILITY AND REHABILITATION CONTEXT

2016· article· en· W2466570649 on OpenAlexfundno aff
Amin Gharebaghi, Mir Abolfazl Mostafavi

Bibliographic record

VenueISPRS annals of the photogrammetry, remote sensing and spatial information sciences · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDimension (graph theory)Perspective (graphical)Context (archaeology)Social environmentOntologyComputer scienceHuman DimensionData scienceKnowledge managementSociologyArtificial intelligenceEpistemologySocial science

Abstract

fetched live from OpenAlex

Abstract. Social dimension of environment is an important aspect that should be reflected in research works related to studying the interactions between human and the environment. However, this dimension is usually neglected when representing the environment in geographic information systems for different applications. For instance, disability as a result of the interaction between human and environment is influenced by social and physical dimensions of environment. Although, this aspect is highlighted in most conceptual disability models by defining various taxonomies of the environment, from ontological perspective justifying and connecting social dimension to the physical dimension of the environment is not clearly determined. Integrating social dimension of the environment with its physical dimension for disability studies is a challenging task, which is the main objective of the present study. Here, we review some of the disability models and their perspective about classifying the environment. Then, from ontological perspective, their limitations are discussed and a new approach for the classification of concepts form the environment is presented. This approach facilitates and simplifies integration of social dimension in ontologies for more effective assessment of disability issue in Geographic Information System.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
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 head0.056
GPT teacher head0.356
Teacher spread0.299 · 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 designOther design
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

Citations6
Published2016
Admission routes1
Has abstractyes

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