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Record W4221056324 · doi:10.33137/ijidi.v5i5.37130

Diversity, equity and inclusion policy texts in Canadian agriculture

2022· article· en· W4221056324 on OpenAlexaffabout
Bobby Thomas Cameron, Ziad Ghaith, Lisa Chilton

Bibliographic record

VenueThe International Journal of Information Diversity & Inclusion (IJIDI) · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsAgricultureEquity (law)CitizenshipPublic policyAgricultural policyDiversity (politics)Political sciencePublic administrationSocial constructionismResearch policyPluralism (philosophy)SociologyEconomic growthSocial scienceEconomicsGeographyPolitics

Abstract

fetched live from OpenAlex

This study explores diversity, equity and inclusion (DEI) policy texts in Canadian agriculture from a policy-as-information perspective. Public policy is a powerful form of information in shaping citizenship behaviour and identity. Borrowing theory from social constructionism and using “policy texts” as data, this article enables us to start to understand the discursive framework constructing under-represented groups in agriculture. The article finds that there is a patchwork quilt approach with DEI agricultural policy in Canada: Federal, provincial and territorial governments and non-governmental organizations are individually pursuing DEI agendas. The conclusion calls for future information research on DEI agricultural policy in Canada, with contributions from academics, practitioners, industry and farmers. The contribution of this article is twofold: It provides policy practitioners with a snapshot of current DEI policies in agriculture across Canada and it attempts to stimulate research and discussion among policy scholars through suggestions for future research.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0100.000
Scholarly communication0.0000.001
Open science0.0020.186
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 head0.011
GPT teacher head0.231
Teacher spread0.219 · 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.

Study designObservational
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

Citations4
Published2022
Admission routes2
Has abstractyes

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