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An Ontological Approach to Analysing Social Service Provisioning

2022· article· en· W4308087270 on OpenAlexaff
Mark S. Fox, Bart Gajderowicz, Daniela Roşu, Alina Turner, Dishu Lyu

Bibliographic record

Venue2022 IEEE International Smart Cities Conference (ISC2) · 2022
Typearticle
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceKey (lock)OntologySPARQLKnowledge managementStakeholderService (business)Context (archaeology)Data scienceWorld Wide WebSemantic WebProcess managementRDFComputer securityBusiness

Abstract

fetched live from OpenAlex

This paper introduces ontological concepts required to evaluate and manage the coverage of social services in a Smart City context. Here, we focus on the perspective of key stakeholders, namely social purpose organizations and the clients they serve. The Compass ontology presented here extends the Common Impact Data Standard by introducing new concepts related to key dimensions: the who (Stakeholder), the what (Need, Need Satisfier, Outcome), the how (Service, Event), and the contributions (tracking resources). The paper first introduces key stakeholders, services, outcomes, events, needs and need satisfiers, along with their definitions. Second, a subset of competency questions are presented to illustrate the types of questions key stakeholders have posed. Third, the extension's ability to answer questions is evaluated by presenting SPARQL queries executed on a Compass-based knowledge graph and analysing their results.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.007
Science and technology studies0.0030.006
Scholarly communication0.0060.011
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.045
GPT teacher head0.266
Teacher spread0.221 · 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".

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Citations1
Published2022
Admission routes1
Has abstractyes

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