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Record W2558644453 · doi:10.1525/gfc.2016.16.4.101

In Search of Lard Time

2016· article· en· W2558644453 on OpenAlexaffabout
George Fogarasi

Bibliographic record

VenueGastronomica The Journal of Food and Culture · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsFleming College
Fundersnot available
KeywordsIconCitationGeorge (robot)DownloadSearch engine optimizationComputer scienceWorld Wide WebInformation retrievalLibrary scienceThe InternetArtificial intelligence

Abstract

fetched live from OpenAlex

Research Article| November 01 2016 In Search of Lard Time George Fogarasi George Fogarasi Fleming College George Fogarasi created and teaches a course called “Food, Booze and Culture” at Fleming College in Canada. As a writer, he has flustered a Bond Girl with a feminist query, traced the cultural contours of subjectivity through the lens of Hello Kitty, and celebrated a Saigon vegetarian cafe staffed by homeless youth and run by an ex-marine and Buddhist nun. He enjoys making the strange familiar and the familiar strange in accessible and entertaining ways. Search for other works by this author on: This Site PubMed Google Scholar Gastronomica (2016) 16 (4): 101–104. https://doi.org/10.1525/gfc.2016.16.4.101 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation George Fogarasi; In Search of Lard Time. Gastronomica 1 November 2016; 16 (4): 101–104. doi: https://doi.org/10.1525/gfc.2016.16.4.101 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentGastronomica Search This content is only available via PDF. © 2016 by the Regents of the University of California. All rights reserved. Please direct all requests for permission to photocopy or reproduce article content through the University of California Press's Reprints and Permissions web page, http://www.ucpress.edu/journals.php?p=reprints.2016 Article PDF first page preview Close Modal You do not currently have access to this content.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score0.182

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 head0.010
GPT teacher head0.197
Teacher spread0.187 · 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 designBench or experimental
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
Published2016
Admission routes2
Has abstractyes

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