Coded-Engagement: data-driven participation in the smart city
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".