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Record W3154898942 · doi:10.24908/iqurcp.10500

A Taste of the Library

2018· article· en· W3154898942 on OpenAlexvenueaboutno aff
A.G. Anderson, Kaitlyn MacDonald, Hannah Thiessen

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsTasteFoodwaysPoliticsNationalismSocial history (medicine)Identity (music)InstinctMedia studiesHistorySociologyAestheticsAnthropologyPolitical sciencePsychologyArtLawEcologyMedicine

Abstract

fetched live from OpenAlex

Eating is one of the most innate human instincts. People need food to survive, and food and the culture around eating have been transformed into indicators of personal, community, and national identity. As an often-overlooked branch of social history, food history provides a critical look into the social nature of our nation’s past, as well as the effect of food on the culture and politics of Canada. It is for these reasons our course has decided to mount an exhibit in Special Collections on the history of food in Canada, showcasing the Library’s historical cookbooks. We will be structuring our exhibit through the lens of the following critical themes: Food Nationalism, Women and Community, Multi-culinary-ism, Business of Food, and Local Food Sources in Kingston, using cookbooks dating as far back as the 19th century. These will be brought together to share a glimpse into the study of food history and its importance within Canadian Social History. Sifting through the impressive assortment of cookbooks that W.D Jordan Rare Books and Special Collections has to offer will lead to the selection and presentation of 25 cookbooks that are an essential representation of the food history of the nation. Representing our classmates, we hope to share an overview of our research with Inquiry@Queen’s and promote our exhibit, which opens two weeks after the conference.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.093
GPT teacher head0.320
Teacher spread0.227 · 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
Published2018
Admission routes2
Has abstractyes

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