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Record W4406003825 · doi:10.46692/9781529233445.007

Racialized Migrant Labour in Organic Agriculture in Canada: Blind Spots and Barriers to Justice

2024· other· en· W4406003825 on OpenAlexaboutno aff
Susanna Klassen

Bibliographic record

Venuenot available
Typeother
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic JusticeAgricultureBlind spotSpotsEnvironmental justicePolitical scienceGeographyDemographic economicsAgricultural economicsEconomicsBiologyLawArchaeologyBotany

Abstract

fetched live from OpenAlex

Organic agriculture represents a long- standing social movement to create an alternative to ecologically and socially exploitative farming. When it emerged in the 1940s, organic principles were focused on soil health and nutrient recycling (Heckman, 2006). However, as the movement gained momentum in the 60s and 70s, anti- corporate sentiment and human health concerns began to take a more prominent role (Obach, 2015). Today, it is clear that not all organic certified agriculture offers a viable alternative to the dominant industrialized food system. While many actors in the organic sector still work to maintain its values- based commitments, growth, market mainstreaming, and corporate cooptation in the sector has undoubtedly complicated the picture (Jaffee and Howard, 2010). Incisive critiques from scholars – including from radical geography and critical food studies – have shown that as organic agriculture entered state regulatory regimes and carved out its own markets, the interests of profit and capital accumulation have, in many cases, eroded its ethical foundations (Sutherland, 2013; Guthman, 2014). Critiques of regenerative and organic agriculture have also drawn attention to its foundations in Indigenous agricultural practices and knowledge, often without acknowledgement (Heim, 2020). Moreover, there is a growing body of scholarship that demonstrates that organic farms do not necessarily offer better working conditions than their non- organic counterparts, as low incomes, musculoskeletal injuries, un(der)paid internships, and otherwise poor- quality jobs are all inequities that persist on organic farms (Harrison and Getz, 2015; Weiler, Otero and Wittman, 2016; Soper, 2019). Yet, adherents to international principles of ‘health’, ‘ecology’, ‘fairness’, and ‘care’ – articulated and promoted by the International Federation of Organic Agriculture Movements (IFOAM) – continue to reject the view that organic agriculture is beyond saving (IFOAM, 2020a). In particular, the principle of fairness has become the focus of increasing discussions within the sector (Kröger and Schäfer, 2014), in part because it has no commensurate requirements in regulated organic standards (Klassen et al, 2023). In other words, despite the organic movement's explicit focus on fairness, it has yet to address the unjust nature of labour in an official way. This chapter builds on previous research conducted by myself and collaborators about the organic movement's efforts to integrate fairness into organic agriculture in Canada (Klassen, Fuerza Migrante, and Wittman, 2022).

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)0.003
metaresearch head score (Gemma)0.007
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 score0.066
Threshold uncertainty score0.481

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0470.012
Scholarly communication0.0070.003
Open science0.0030.011
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0090.000

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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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