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Record W4413009852 · doi:10.1016/j.futures.2025.103679

Shaping circular futures: The role of future-making practices in the transition to circular food systems

2025· article· en· W4413009852 on OpenAlexaff
H.H.E. van Zanten, Anita Frehner, Adrian Müller, Olivier Ejderyan, Vivian Valencia, Jessica Duncan

Bibliographic record

VenueFutures · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsBishop's University
Fundersnot available
KeywordsFutures contractTransition (genetics)Circular economySociologyPolitical scienceEconomicsBiologyEcologyFinancial economics

Abstract

fetched live from OpenAlex

Visions of circular food systems have become increasingly central to debates about sustainability, guiding strategies to reduce waste and regenerate resources. Among the at times conflicting visions that diverge in values and priorities, the transition toward circular food systems remains uncertain. We examine how everyday future-making practices reconfigure relations with waste and shape circular transitions in Switzerland. Drawing on Social Practice Theory and the concept of transformativity, we analyze how actors perform practices that reshape how waste is encountered, valued, and integrated into circular material flows in everyday life. Based on qualitative fieldwork conducted in 2023 with four cases from the Swiss food system including fertilizer production from urine, urban aquaponics, food waste redistribution, and biodynamic CSA farming, we show how different practices render alternative waste values tangible and engage people in circular futures-in-the-making through habitual, planned, and experimental modes of practices. In doing so, we highlight the everyday as a key site where contested circularity visions are negotiated, adapted, and implemented.

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)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.024
Scholarly communication0.0150.010
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.021
GPT teacher head0.258
Teacher spread0.237 · 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 designTheoretical or conceptual
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

Citations4
Published2025
Admission routes1
Has abstractno

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