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Record W4391061571 · doi:10.1080/15528014.2023.2296730

Media-ting Austerity Feeding: second-hand Infant Food Exchange Online in Canada

2024· article· en· W4391061571 on OpenAlexafffundabout
Lesley Frank

Bibliographic record

VenueFood Culture & Society · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsAcadia University
FundersAcadia University
KeywordsAusterityMainstreamCurrencyCapital (architecture)BusinessMarketingAdvertisingPoliticsEconomicsPolitical scienceGeographyMonetary economics

Abstract

fetched live from OpenAlex

This paper makes visible austerity-based infant food exchange as a contemporary food acquisition practice outside of commercial and regulated foodscapes. It presents results from a netnography conducted within two popular online platforms in Canada between April 2017 and February 2018: the Kijiji classified advertising site, and Facebook. Qualitative analysis of over 2000 user generated secondhand exchange ads show practices from selling, trading, sharing, and seeking whereby infant foods works as a form of currency to acquire either economic capital when sold, an alternative food capital when traded, or are gifts in the moral economy of exchange. While infant food exchange is often collaborative in nature, it is austerity-driven involving different forms of capital parents have or seek to accomplish the responsibility of feeding. As excluded consumers, posters are motivated by a complex mixture of desperation, innovation, ecological concerns, and morality to care for others when mainstream food access is out of reach within the current political and economic system.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.039
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0140.005
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.000

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 head0.021
GPT teacher head0.220
Teacher spread0.199 · 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
Published2024
Admission routes3
Has abstractyes

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