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Record W4296103518 · doi:10.3390/pr10091810

Bibliometric Analysis of Current Status of Circular Economy during 2012–2021: Case of Foods

2022· article· en· W4296103518 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProcesses · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCircular economySustainabilityScopusCitationBibliometricsResource (disambiguation)Regional scienceBusinessGeographyPolitical scienceEconomyLibrary scienceEconomicsComputer scienceEcology

Abstract

fetched live from OpenAlex

Food is usually recognized as a limited resource in sustainability, recognized by the United Nations (UN) Sustainable Development Goal (SDG) 2, which aims to achieve zero hunger. It is expected that the circular economy (CE) can improve processes related to food production. The objective of the present study was to apply a bibliometric approach using VOSviewer visualization software as a research tool to analyze the relevant literature from the Scopus database in the field of circular economy and its relation to improvements in food issues throughout 2012–2021. A total of 1316 articles were analyzed. Italy was the most productive country with 313 articles, with Wageningen University & Research the organization with the most publications with 39 articles. The top 10 institutions originated from Italy, The Netherlands, France, and Denmark. Sustainability was the most productive journal with 107 articles. Bioresource Technology had the highest average citation frequency (44.68). The study helps initiate investigations to adapt the results obtained and create new strategies for increasing food circular economy efforts.

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.

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 categoriesBibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.062
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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