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Coded-Engagement: data-driven participation in the smart city

2020· dissertation· en· W7017785815 sur OpenAlexaff

Notice bibliographique

RevueeScholarship@McGill (McGill) · 2020
Typedissertation
Langueen
DomaineEngineering
ThématiqueSmart Cities and Technologies
Établissements canadiensMcGill University
Organismes subventionnairesnon disponible
Mots-clésBig dataProcess (computing)Data collectionField (mathematics)Smart cityInterface (matter)Set (abstract data type)Computational intelligenceHuman intelligence
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Algorithm -A series of operations for carrying out a certain type of task, usually in a computational context.Application (Apps/Application) -Computer programs designed to perform a group of integrated activities for the benefit of the user. Application Programming Interface (API) -A set of access points, software libraries, protocols, and/or tools that allow for integrating different data, software, and hardware systems. Artificial Intelligence (AI)-A broad field of computational sciences focused on programming machines to act with an apparent intelligence resembling those of human cognitive functions.There are varying definitions of AI that lead to a range of meanings in contemporary use, including fields of machine-learning, data mining, and statistics.See also: machine learning, data mining.Big Data -A popular marketing phrase, with various definitions in industry and academic literature.It generally refers to the collection of data that had been impractical prior to the proliferation of computational resources.Often big data is discussed about the 4 V's: volume, variety, veracity, and velocity.In this way, big data refers both the collection and nature of data sets that are so large and complex they become difficult to capture, transfer, store, process and interpret with traditional data processing applications.See Also: VGI, User-generated content. Business Intelligence (BI)-A form of data analysis narrowly focused on business performance and optimization.Citizen or Civic Engagement -A process and practice that seeks to include residents in the decision-making around city building.This can be led by individuals or groups, by public or private organizations, or by the government.See Also: Public Participation, Citizen CentricCitizen-centric -An approach to the delivery of public services based on solving the needs and challenges of the people they serve.It is used to increase public satisfaction, improve efficiency and reduce costs, often through a technologically focused lens.See also: Smart City, City-as-a-Service.Citizen-focused -Focusing on priorities and solutions at the individual citizen level.City-as-a-Service -Combines Infrastructure-as-a-Service (IaaS) and Software-as-a-Service (SaaS) technologies for use as a common, city-wide platform for the deployment of integrated smart city technologies.A common reference in this context is an "operating system" for the city.vi Community of Interest -A social group sharing interests on various topic matters relevant to their daily lives.Community of Practice -Individuals who either collectively or independently engage in similar activities.Connectivity -The ability of individuals and devices to connect to communications networks, services, or each other.Data Analysis -This discipline is the "little brother" of data science.Data analysis is focused more on answering questions about the present and the past.It uses less-complexstatistics and generally tries to identify patterns that can improve an organization.Data Exploration -The part of the data science process where a scientist will ask basic questions that help in understanding the context of a data set.What is learned during the exploration phase will guide more in-depth analysis later.Further, it helps in situations where results may be surprising, thereby warranting further investigation.Data Mining -Generally, the use of computers to analyze large data sets to look for patterns that let people make business decisions.While this may appear to be similar to data science, popular use of the term is much older, dating back at least to the 1990s.See also: data science Data Science -Given the rapid expansion of the field, the definition of data science can be hard to nail down.Basically, it is the discipline of using data and various forms of advanced statistics to make predictions.Data science is also focused on creating understanding among potentially poor-quality and disparate data. Data Set -A collection of data.Data Visualization -The art of communicating meaningful data visually.This can involve infographics, traditional plots, or even full data dashboards.Data-Driven -The use of data to support decisions, policies, and actions as evidence-based choice making.Hyper-local data -Data originating or circulated within a very small geographical area, such as a street or apartment block. Information and Communications Technology (ICT)-The integration of telecommunications, computers, and associated enterprise software, middleware, storage, and audio-visual systems that enable users to access, store, transmit, and manipulate information.Infrastructure -Both the physical and virtual resources, facilities, and systems serving a city. Internet-of-Things (IoT) -In a general context, the IoT is the provision of networked capability to electronic devices and everyday objects for an interrelated system of computing vii devices, sensor technologies, algorithms, and people.See also: Big Data, Citizen-as-SensorInteroperability -The capacity to integrate networks, computers, and systems for the sharing of resources and exchange of information.See also: Siloed Cities, Big Data, IoT, API Machine Learning -The use of data-driven algorithms that perform better as they have more data to work with, "learning" (that is, refining their models) from this additional data.See also: algorithm, data mining, artificial intelligence Model -The specification (mathematical or probabilistic) of the relationship that exists between different variables.Since "modelling" has a variety of meanings, the term "statistical modelling" is often used to more accurately describe the type of modelling undertaken by data scientists.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,021
Score d'incertitude au seuil0,071

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0060,006
Communication savante0,0110,011
Science ouverte0,0020,019
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,0210,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,053
Tête enseignante GPT0,272
Écart entre enseignants0,219 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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