Trying to Measure Local Well-Being: Indicator Development as a Site of Discursive Struggles
Bibliographic record
Abstract
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.
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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 | 0.151 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.023 | 0.065 |
| Scholarly communication | 0.024 | 0.022 |
| Open science | 0.004 | 0.031 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".