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Record W2147543817 · doi:10.1068/c10127

Trying to Measure Local Well-Being: Indicator Development as a Site of Discursive Struggles

2013· article· en· W2147543817 on OpenAlexaff
Karen Scott, Derek Bell

Bibliographic record

VenueEnvironment and Planning C Government and Policy · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsAgriculture Food and Rural Development
FundersNewcastle University
KeywordsSustainabilityLocal governmentPolitical scienceEthnographyProcess (computing)PoliticsLocal authorityGovernment (linguistics)Quality (philosophy)SociologyPerformance indicatorPublic administrationEconomicsManagementLaw

Abstract

fetched live from OpenAlex

The New Labour government in the UK encouraged all local authorities to develop quality-of-life indicators. Development of these indicators was intended to engage local people in a shared vision for their area and to effect improvements in local well-being and sustainability. However, international research has reported a failure of such instruments to generate concrete policy change. This paper reports on a three-year ethnographic study in one local authority in North East England which took a discursive approach to analysing indicator development. The research shows how indicator development acted as a ‘site of struggle’ between competing discourses. These discursive struggles may have hampered the development of a set of indicators, but they allowed different conceptions of well-being, participation, indicators, and the policy-making process to be discussed and deliberated, inducing discursive shifts in the political arena. Policy makers and scholars should therefore place more focus on the process of developing indicators rather than the indicators that are produced, as it is these which have the potential to produce longer term effects on policy making.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.270
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations26
Published2013
Admission routes1
Has abstractyes

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