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Record W2997237007 · doi:10.24847/66i2019.235

PITAS AND PASSPORTS: ARAB FOODWAYS IN THEWINDSOR-DETROIT BORDERLANDS

2019· article· en· W2997237007 on OpenAlexaffabout
Robert L. Nelson

Bibliographic record

VenueMashriq & Mahjar Journal of Middle East and North African Migration Studies · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsFoodwaysWindsorEthnic groupPolitical scienceNegotiationCitizenshipDiasporaSociologyPolitical economyEthnologyGender studiesLawPoliticsAnthropology

Abstract

fetched live from OpenAlex

Virtually all North American border foodways literature focuses on the U.S. Southwest, where an international boundary runs through an ethnic foodway. This research is enormously valuable, but Mexican food in the Southwest does not tell us much about a diasporic border-food community. What do we learn when we study an international boundary running through an ethnic minority, such as the Arab community that exists on both sides of the Windsor/Detroit border? When a foodway already struggles to find its identity within a hegemonic culture, how does that food community negotiate an international state difference right in its midst? Before 9/11, a “soft” border allowed for the existence of a large, interactive, regional cross-border Arab foodway. Since then, the thickening of that border has rapidly accelerated the formation of an “Arab Canadian” foodway in Windsor.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.390

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0060.003
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.041
GPT teacher head0.216
Teacher spread0.175 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations1
Published2019
Admission routes2
Has abstractyes

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