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Record W2802220349 · doi:10.9707/1944-5660.1410

Newfoundland and Labrador’s Vital Signs: Portrait of a Foundation-University Partnership

2018· article· en· W2802220349 on OpenAlexaffabout
Ainsley Hawthorn, Sandra Brennan, Robert E. Greenwood

Bibliographic record

VenueThe Foundation Review · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsFoundation (evidence)General partnershipPortraitVital signsGeographyPolitical sciencePublic administrationSociologyLawArchaeologyMedicine

Abstract

fetched live from OpenAlex

Vital Signs, a national program of Community Foundations of Canada, produces annual reports of the same name that examine the quality of life using statistics on fundamental social issues. With these reports, community foundations are able to present a comprehensive and balanced picture of well-being in their communities. The Vital Signs report for Newfoundland and Labrador is produced in partnership between the Community Foundation of Newfoundland and Labrador and the Leslie Harris Centre of Regional Policy and Development, a university research unit with expertise in both promoting community-based research and making academic information accessible to the general public. This article examines the origins of this collaboration and the lessons that have been learned from it, and discusses how the report addresses a need for community knowledge in Newfoundland and Labrador.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0060.008
Scholarly communication0.0080.003
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.048
GPT teacher head0.336
Teacher spread0.289 · 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 designNot applicable
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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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