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Record W4386588783 · doi:10.5539/jsd.v16n5p128

From Benefits to Value(s): Biogas Systems Valuation Practices from a Swedish Regional Perspective

2023· article· en· W4386588783 on OpenAlexvenueno aff
Nancy Brett

Bibliographic record

VenueJournal of Sustainable Development · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsnot available
FundersEnergimyndighetenLinköpings Universitet
KeywordsValuation (finance)FormalitySustainabilityPerformative utteranceBiogas productionBiogasBusinessEnvironmental economicsEconomicsEnvironmental resource managementPolitical scienceEpistemology

Abstract

fetched live from OpenAlex

Local and regional contexts are essential spaces to promote sustainable energy transitions. Within this space, biogas production and use are one choice considered part of a regional sustainable transition. The valuation of different options has become a recurrent and vital activity in connection with strategic energy decisions in the public sector. However, capturing the diverse benefits of biogas is challenging. This study scrutinises the socially and politically bounded practice that (re)produces value(s) by examining the valuation practices related to biogas performed by regional and national actors in Sweden. It finds that although the valuation practice has a degree of formality, it still reflects local needs and specific contexts. A wide range of benefits undergoes a translation process to produce value(s) using qualitative and quantitative rationalities. Still, there is tension between the desire to prioritise quantitative methods while at the same time acknowledging that some benefits are too difficult to quantify. This paper contributes to the literature on the valuation of non-market objects by highlighting the connections between established scientific and economic procedures and performative processes and the importance of rejecting a binary view of quantification and qualification.

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)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0030.018
Scholarly communication0.0150.009
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.292
Teacher spread0.242 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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