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Record W2081115135 · doi:10.1525/gfc.2010.10.1.117

Losing the Space Race

2010· article· en· W2081115135 on OpenAlexaff
kay sexton

Bibliographic record

VenueGastronomica The Journal of Food and Culture · 2010
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsNovelis (Canada)
Fundersnot available
KeywordsIconCitationDownloadSpace (punctuation)CONTESTWorld Wide WebComputer scienceLibrary scienceArtPolitical science

Abstract

fetched live from OpenAlex

Research Article| February 01 2010 Losing the Space Race kay sexton kay sexton kay sexton is a British writer whose fiction has been chosen for over thirty anthologies in the five years she has been writing. Her unpublished novel, ““Gatekeeper,”” was shortlisted for the Dundee International Book Prize; she won the Fort William Festival Contest. Sexton is currently working on a second novel about pornography and rivers in 1920s Hampshire, as well as blogging about food, gardens, and growing things. Search for other works by this author on: This Site PubMed Google Scholar Gastronomica (2010) 10 (1): 117–118. https://doi.org/10.1525/gfc.2010.10.1.117 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 kay sexton; Losing the Space Race. Gastronomica 1 February 2010; 10 (1): 117–118. doi: https://doi.org/10.1525/gfc.2010.10.1.117 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. ©© 2010 The Regents of the University of California. All Rights Reserved.2010 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score0.260

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 head0.008
GPT teacher head0.188
Teacher spread0.180 · 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
Published2010
Admission routes1
Has abstractyes

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