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Record W4399988723 · doi:10.1016/j.agsy.2024.104032

Dynamic evaluation of agricultural research for development supports innovation and responsible scaling through high-level inclusion

2024· article· en· W4399988723 on OpenAlexaff
John Gargani, Petronella Chaminuka, Robert A. McLean

Bibliographic record

VenueAgricultural Systems · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsInternational Development Research CentreImpact
Fundersnot available
KeywordsInclusion (mineral)ScalingAgricultureComputer scienceRegional scienceSociologySocial scienceGeographyMathematics

Abstract

fetched live from OpenAlex

Innovators want to scale the impact of innovations responsibly, but the meaning of “responsible” is elusive. It entails the high-level inclusion of stakeholders, but there is no agreed-upon standard that defines a “good-enough” level. Our objectives are to (1) provoke a conversation about what it means to scale the impact of agricultural innovations responsibly and (2) suggest dynamic evaluation as one way to promote responsible scaling because it facilitates the leadership of the people affected. Over 200 projects funded by IDRC to scale the impact of research for development in the Global South were reviewed. Research products were iteratively developed with Southern innovators and Northern funders who offered structured feedback. The dynamic scaling systems model is one research product. It can guide the people affected as they lead a process of “conjecturing” about scaling effects in complex settings. The resulting conjectures inform the dynamic evaluation of scaling, as well as planning and management. We illustrate the application of the model with a hypothetical and real example. Scaling is an integral part of agricultural innovation, and dynamism is an emerging concept that informs the evaluation, planning, and management of scaling. The dynamic scaling systems model supports the high-level inclusion of the people affected in ways that respect local knowledge and the risks associated with complex settings. It helps innovators scale more responsibly, even though the precise meaning of “responsible” remains elusive. • Innovators want to scale the impacts of agricultural innovations responsibly, but the meaning of “responsible” is elusive. • Innovators cannot fully achieve ideal inclusion—everyone affected participates at the highest level with complete control. • Dynamic evaluation helps innovators get closer to the ideal because, among other things, it promotes Conjecturing . • Conjecturing is a collaborative process of anticipating scaling effects that is led by the people affected. • The dynamic scaling systems model guides conjecturing by framing questions about potential scaling effects in complex systems.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.827
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.105
GPT teacher head0.347
Teacher spread0.242 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations5
Published2024
Admission routes1
Has abstractyes

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