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Record W2313044991 · doi:10.1177/0169796x14550933

Popular Fiction and Development Studies

2014· article· en· W2313044991 on OpenAlexaff
David Lempert

Bibliographic record

VenueJournal of Developing Societies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican cultural and philosophical studies
Canadian institutionsInternational Development Research Centre
Fundersnot available
KeywordsField (mathematics)Techno-thrillerFiction theoryLiterary fictionSociologySci-FiAestheticsEpistemologyLiteratureHistoryEngineering ethicsFantasyLiterary criticismArtPhilosophyEngineering

Abstract

fetched live from OpenAlex

The essay uses two recent works of fiction as a takeoff for a critique of Development Studies, suggesting that what is presented in the form of fiction is often closer to reality than what the discipline offers as fact. The review of two novels by former US Peace Corps volunteers offers clear examples of how fiction in the field of “development” can offer truths that are not presented in academic work. This essay suggests how fiction can help invigorate the discipline of Development Studies, offers a list of examples, and also suggests how fiction should not be used. This essay challenges scholars in the field to draw upon insights from fiction and to review fiction works while encouraging publishers to widen their perspectives and present works in new genres in the field of “development.” The essay also notes how related genres like development “diaries” can also be used as a reality check on the discipline and as a way to infuse new ideas into this field.

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)0.009
metaresearch head score (Gemma)0.027
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: none
Teacher disagreement score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.005
Science and technology studies0.0090.043
Scholarly communication0.0110.012
Open science0.0010.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.100
GPT teacher head0.344
Teacher spread0.244 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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