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A Needs Assessment for a Local Social Policy Data Sharing Program

2023· article· en· W4391395720 on OpenAlex
Shannon L. Sibbald, Kimia Sedig, Ross J. Graham, Simoni Haji, Prabhnoor Chhatwal, Daniel J. Lizotte, Jasminder Bhatti

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Planning and Policy / Aménagement et politique au Canada · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsRegional Municipality of WaterlooWestern University
Fundersnot available
KeywordsData sharingComputer sciencePolitical scienceMedicine

Abstract

fetched live from OpenAlex

Planners seek collaborative and diverse strategies to address complex challenges across Canada. In the Region of Waterloo, local governments and stakeholders adopted The Waterloo Region Community Data Program (WRCDP), a social policy data-sharing system for information on economic and social development. This study examined the needs of Community Data Program (CDP) users to investigate whether CDPs are utilized effectively. We considered how optimal use of CDP data may advance planning to resolve structural and behavioral challenges in municipalities. We surveyed 17 participants from the WRCDP to assess needs of organizational members regarding accessing data, data analysis, and networking. Participants expressed enthusiasm for the CDP’s potential but lacked training in accessing and analyzing available data. A limitation of this study is small participant sample size and how results may not be generalizable to other locations. Organizational members remained optimistic about the system’s potential for planning and policy when provided with the necessary support.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.147
GPT teacher head0.466
Teacher spread0.319 · 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