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The Canadian Index of Wellbeing: A Better Way to Assess and Communicate the Value of Libraries

2022· article· en· W4210780252 on OpenAlexaffvenueabout
Cara Bradley

Bibliographic record

VenuePartnership The Canadian Journal of Library and Information Practice and Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsVitalityGross domestic productDominance (genetics)Value (mathematics)Index (typography)DemocracyMetric (unit)Public relationsEconomicsBusinessEconomic growthPublic economicsPolitical scienceMarketingComputer scienceLawWorld Wide Web

Abstract

fetched live from OpenAlex

Historically libraries have struggled to communicate their value in ways meaningful to both policy-makers and the general public. Traditional measures like collection and circulation counts, while useful, fail to capture libraries’ full impact on the lives of their users. The recent dominance of Gross Domestic Product (GDP) as the prevailing metric for policy and decision-making frames library value in exclusively economic terms. However, it is overreliance on economic measures like GDP in library assessment that leads to their undue underfunding. Meanwhile a tool like the Canadian Index of Wellbeing (CIW) is a credible alternative metric that shifts the focus from the purely economic toward additional facets of life. Developed through a broad cross-Canada consultation process, the CIW uses eight domains affecting wellbeing: community vitality, democratic engagement, education, environment, healthy populations, leisure and culture, living standards, and time use. Compared with the narrow economic focus of GDP, the CIW is a powerful tool to communicate the true value of public libraries and the impact they have on their users.

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 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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0010.013
Open science0.0010.000
Research integrity0.0000.001
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 head0.116
GPT teacher head0.374
Teacher spread0.258 · 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 teacher head, not a consensus.

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

Citations2
Published2022
Admission routes3
Has abstractyes

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