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Record W4401930555 · doi:10.3389/fsufs.2024.1437910

Does sustainability matter in the global beer industry? Bibliometrics trends in recycling and the circular economy

2024· article· en· W4401930555 on OpenAlexaboutno aff
María Cristina Ravanal, Jean Pierre Doussoulin, Benoît Mougenot

Bibliographic record

VenueFrontiers in Sustainable Food Systems · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityCircular economyBibliometricsEconomic geographyEconomyAgricultural economicsEconomicsBusinessNatural resource economicsComputer scienceEcologyLibrary science

Abstract

fetched live from OpenAlex

It is commonly known that the beer industry, like many other companies in the food sector, has been encouraged to improve their sustainability and waste management requirements. This study intends to fill the gap referring to the Brewery Industry’s impact on academics from 1972 to 2022 related to recycling and waste management issues. The above research utilizes bibliometric analysis via Biblioshiny and the Scopus publications database, as well as an online interface for Bibliometrix analysis. For studying the Global Brewery Industry literature, this method offers a viable alternative to traditional bibliometric analysis. Among the findings we can mention are that most Brewery Industry inquiries were distributed by the “Bioresource Technology” and the most globally cited document is the book titled “Brewing: Science and Practice” written by De Briggs in 2004. European countries such as Italy, Spain and Portugal hold a strong academic collaboration with the Americas (mainly Canada, United States and Brazil). Another interesting finding indicates that the circular economy and recycling are not very present topics in the scientific literature, maybe because sustainability is a subject of recent discussion and study in the brewing industry.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.017
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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.

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

Citations6
Published2024
Admission routes1
Has abstractyes

Explore more

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