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The Crowding Effects of Basic and Applied Research: A Theoretical and Empirical Analysis of an Agricultural Biotech Industry

2005· article· en· W2020745465 on OpenAlex
Stavroula Malla, Richard Gray

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

VenueAmerican Journal of Agricultural Economics · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsUniversity of SaskatchewanUniversity of Lethbridge
Fundersnot available
KeywordsCrowding outProductivityIncentiveEconomicsAgricultureVariety (cybernetics)Empirical researchEconometric modelPublic economicsConsistency (knowledge bases)Industrial organizationEconometricsMarketingMicroeconomicsBusinessComputer scienceMathematicsMacroeconomicsStatisticsBiology

Abstract

fetched live from OpenAlex

Abstract Game theory is used to examine the incentives for private firms to fund applied research to improve differentiated crop varieties sold to compete with a public generic variety. We distinguish between applied research, modeled as a stochastic search process, and basic research, which improves applied research productivity. Propositions derived from the theoretical model are tested using empirical evidence from the canola crop research industry. The results show consistency between the analytical findings and the econometric results, supporting the validity of the framework and underlining the need to disaggregate the crowding effects of basic and applied public 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.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.270
Teacher spread0.246 · 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