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Record W3007726079 · doi:10.5281/zenodo.3539254

International Research Infrastructure Landscape 2019

2019· article· en· W3007726079 on OpenAlexfundno aff
Ari Asmi, Lorna Ryan, Emmanuel Salmon, Christine Kubiak, Serena Battaglia, Miriam Förster, J. Stefan Dupre, W. G. Stirling, Kurt W. Clausen, Mikkel Stein Knudsen, Marianna Birmoser Ferreira-Aulu, Elizaveta Shabanova-Danielyan, Weiqing Wang, Jyrki Luukkanen, Jari Kaivo‐oja, Carla Baldovin, Marjan Timmer, Rob van der Meer, René Vermeulen, Ivana Ilijašić Veršić, Franco Niccolucci, Gergely Sipos, Roberta Piscitelli, Jostein K. Sundet

Bibliographic record

VenueTyöväentutkimus Vuosikirja · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInnovation Policy and R&D
Canadian institutionsnot available
FundersCollege of Pharmacy, University of MichiganCollege of Veterinary Medicine, Cornell UniversityNational Institutes of HealthResearch Institute of Economy, Trade and IndustryFudan UniversityPennsylvania State UniversityUniversity of TokyoEuropean CommissionPartnership for Advanced Computing in Europe AISBLUniversity of ChicagoUniversity of PennsylvaniaAustralian National UniversityUniversity of MichiganYork UniversityNational Science Foundation
KeywordsPerspective (graphical)Regional scienceEnvironmental resource managementGeographyEnvironmental planningComputer scienceEnvironmental science

Abstract

fetched live from OpenAlex

A landscape report of major international (outside of Europe) research infrastructures. The report is the final product of the RISCAPE project, funded by the European Commission H2020 programme. This is the consolidated version, with the appendices included, and represents the version available 30/12/2019.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.212

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.011
Science and technology studies0.0010.000
Scholarly communication0.0110.005
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0630.047

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 head0.040
GPT teacher head0.295
Teacher spread0.255 · 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.

Study designObservational
DomainEvaluation
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

Citations3
Published2019
Admission routes1
Has abstractyes

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