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Record W3197171891

Learning for biocultural design: community kitchens as innovation spaces for small-scale food production in Manitoba

2020· dissertation· en· W3197171891 on OpenAlexfundaboutno aff
Emmanuella Addae-Wireko

Bibliographic record

VenueMspace (University of Manitoba) · 2020
Typedissertation
Languageen
FieldEngineering
TopicDesign Education and Practice
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Manitoba
KeywordsScale (ratio)GeographyProduction (economics)CartographyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Community kitchens have received much attention in the literature, yet their use for commercial purposes by small scale food producers/ processors are under-reported. The purpose of this project was to understand the role played by commercial community kitchens in Manitoba. Commercial community kitchens are a type of innovation space where small-scale food business owners develop product ideas and process raw materials into finished products. Primary data collection methods included the use of semi-structured interviews with eleven small-scale food business owners who produce and process a variety of food products (e.g. kombucha drinks, hummus, almond butter spreads, and gluten-free perogies). Results indicated that the frequency of commercial community kitchens used for these food products ranges from seasonal to yearly use to periodic year-round use. Some business owners stopped using particular commercial community kitchens, combine the use of commercial community kitchens with other facilities, or use more than one commercial community kitchen. Some have stopped using commercial community kitchens because space, storage, tools, equipment, or resources were not adequate to their needs, or the rental cost was too high. The main reason for using commercial community kitchens was the need for government-certified community kitchens, which meet Manitoba’s health standards and regulations, to commercialize food products. Based on business owner interviews, the research suggests that commercial community kitchens can improve their services by increasing storage space, providing relevant tools and equipment for their users, and implementing programs to build user capacity of the kitchen facilities and equipment.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.056
GPT teacher head0.244
Teacher spread0.188 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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