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Record W7001991200

Materializing Memory Through Quilted Cod: “Making Fish” as Memorial

2024· article· en· W7001991200 on OpenAlexaboutno aff

Bibliographic record

VenueLincoln (University of Nebraska) · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTreasureClothingFishingGold rushFish <Actinopterygii>Oral historyPresentation (obstetrics)Government (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

Cod is so central to the history of Newfoundland that the process of salting and drying it is there called “making fish.” For nearly 500 years, Newfoundlanders made fish to ship globally until 1992 when the Canadian government announced an indefinite and ongoing moratorium on its cod fishery, changing the island forever. My presentation is about my own “making fish” from textiles while researching the history of cod in Newfoundland textile arts. When I first cut pieces of fabric into the triangular shapes of a salted cod split, I wanted a garland of quilted fish for my Christmas tree in Montreal. The repetitive gestures of cutting and stitching made me reflect on my ancestors, who wove their own fishing nets to catch and make fish, and who were terribly exploited by the truck system that kept them impoverished despite their labour. I also considered cod’s role in colonialism: it was sent to the West Indies in exchange for molasses and rum—still essential ingredients in Newfoundland—during slavery. I recalled how the moratorium highlighted the urgency of sustainable practices, and remembered my awe at my late Uncle John’s knowledge as he showed me how to cod jig during the summer food fishery. I began integrating his clothes into my quilted fish, making larger splits as comfort objects. A nostalgic impulse thus turned into an art project meant to sustain cultural heritage and personal connections to the past while acknowledging the complex historical meanings of this former food staple.

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 score0.540
Threshold uncertainty score0.568

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 head0.009
GPT teacher head0.298
Teacher spread0.289 · 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
Published2024
Admission routes1
Has abstractyes

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