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Record W4401215899 · doi:10.1002/jid.3929

When development cooperation principles clash: Country ownership and LGBTQI+ inclusion in hostile environments

2024· article· en· W4401215899 on OpenAlexafffund
Stephen Brown

Bibliographic record

VenueJournal of International Development · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of Ottawa
FundersDeutsche ForschungsgemeinschaftSocial Sciences and Humanities Research Council of CanadaUniversity of CambridgeUniversitätsallianz RuhrLeverhulme Trust
KeywordsInclusion (mineral)BusinessEconomic growthPolitical scienceInternational tradeEconomicsPsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract In some instances, two basic development cooperation principles appear to be in direct contradiction: on the one hand, the Sustainable Development Goals prescribe universal social inclusion under the leitmotif of “leave no one behind”, mandating an emphasis on the most marginalized. On the other hand, the cornerstone of development cooperation is “ownership”, which recognizes that countries must be free to choose their own priorities and strategies. To what extent can these two principles be reconciled in “hostile environments”, places where certain groups, such as LGBTQI+ people, are marginalized and even persecuted and criminalized? I argue that, while the SDGs are clear about the need for radical inclusion, the ownership principle lacks precision about who “owns” the concept. Adopting an emancipatory conceptualization of ownership, under which the ultimate beneficiaries should be the ones to determine priorities and strategies, eliminates the apparent contradiction and legitimizes support to marginalized groups even if their own governments disagree.

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)0.015
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.029
Scholarly communication0.0090.008
Open science0.0010.021
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.023
GPT teacher head0.283
Teacher spread0.260 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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