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Record W4405696204 · doi:10.1080/00343404.2024.2435517

Making the Ontario craft beer market

2024· article· en· W4405696204 on OpenAlexaffabout
Kevin T. Roy, Harald Bathelt

Bibliographic record

VenueRegional Studies · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCraftBusinessCompetition (biology)Context (archaeology)MarketingProduct (mathematics)Face (sociological concept)Production (economics)Product marketLicenseeDistribution (mathematics)Industrial organizationCommerceEconomicsIncentiveMarket economySociologyMicroeconomics

Abstract

fetched live from OpenAlex

The Ontario craft beer industry developed as an adjacent localised market in competition with the conventional beer industry that largely controlled regional market access. Using a markets-as-practice perspective, we conduct interviews with craft brewers to investigate institutional work and market-making processes in distribution. Our study shows how entrepreneurs employ practices of creating social relations with customers through regular face-to-face interaction to overcome market blockages in a localised market context. The results indicate this was enabled by establishing personal relations with nearby licensees and customers, strategic selection of brand ambassadors, product development through close brewer–licensee interaction, and employing dedicated sales representatives with production know-how.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

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

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 head0.117
GPT teacher head0.302
Teacher spread0.185 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2024
Admission routes2
Has abstractyes

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