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Record W2727805847 · doi:10.1080/08039410.2017.1345786

The Geography of Development Studies: Leaving No One Behind

2017· article· en· W2727805847 on OpenAlex
Logan Cochrane, Alec Thornton

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueForum for Development Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsCarleton University
Fundersnot available
KeywordsStatus quoCreativityPolitical sciencePrioritizationInclusion (mineral)SociologyEngineering ethicsCritical reflectionPublic relationsSustainable developmentSet (abstract data type)Millennium Development GoalsEconomic growthEnvironmental ethicsPublic administrationSocial sciencePedagogyManagement scienceLawPovertyEngineering

Abstract

fetched live from OpenAlex

Whereas the Millennium Development Goals sought reductions, the Sustainable Development Goals have set forth bold new objectives of leaving no one behind. This Commentary explores the continued geographic prioritization and exclusions within development studies research and some of the causes. The status quo is entrenching exclusion. A transformation of research, and the research community, is required to ensure that no one is left behind. Providing the evidence to support decision-making that is equitable and inclusive necessitates critical reflection of the exclusions that exist, along with innovation and creativity in how the research community can address gaps and support the more inclusive SDG agenda. Thought leadership and evidence will be the foundation that transforms our research and practice – if we, as a community of researchers, heed the call.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0100.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.103
GPT teacher head0.389
Teacher spread0.286 · 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