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Record W2805486413 · doi:10.1145/3209281.3209379

HandYwiN

2018· article· en· W2805486413 on OpenAlex
Arbi Chouikh

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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsnot available
FundersUniversité Laval
KeywordsComputer science

Abstract

fetched live from OpenAlex

The concept of Smart cities is increasingly gaining a widespread development and interest from scholars and government representatives. This growth transformed largely the smart city into a "lifestyle" of cities' organizations and individuals who look for improved governance and urban life harnessed by Information and Communication Technologies (ICT). Given the administrative, the socioeconomic and the urban nature of cities, they are becoming more complex and facing continuous challenges. The mobility is one of the stands that make cities smarter, more available and more affordable. Accessibility is one of the gaps that need to be addressed from urban and social point of view. In this paper, we focus on persons with disabilities to provide them access to the urban environment. To this end, we present a "Crowdsourcing mapping" based platform called "HandYwiN" and see the added value of this initiative to the mobility of persons with disabilities in smart cities. We will focus on the adopted methodology to determine and map the accessibility of public and private spaces for people in wheelchairs.

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)0.000
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.545
Threshold uncertainty score0.482

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 head0.009
GPT teacher head0.186
Teacher spread0.177 · 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

Quick stats

Citations3
Published2018
Admission routes1
Has abstractyes

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