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The Emilia-Romagna System for Start-Up Growth

2015· article· en· W2596734834 on OpenAlex
Sara Monesi, Sveva Ruggiero, Lucie Sanchez

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

VenueSymphonya Emerging Issues in Management · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse academic and cultural studies
Canadian institutionsASTER
Fundersnot available
KeywordsGeneral partnershipBusinessSoftware deploymentVariety (cybernetics)Advanced Spaceborne Thermal Emission and Reflection RadiometerEngineeringComputer scienceFinance

Abstract

fetched live from OpenAlex

EmiliaRomagnaStartup (ERSU) is one of the pillars of the Emilia-Romagna regional policy to promote innovative business creation. Launched in 2011, it represents nowadays one the most developed regional instruments at EU level to support start-up creation through information, orienteering, services provision and networking opportunities. The initiative is coordinated by ASTER, the regional Consortium for Innovation and Technology Transfer. Designed by ASTER Start-up Dept on the basis of an intensive benchmark activity to provide up-to-date services, today it is constantly improving the variety and the quality of its offer which is directed to start-ups and business projects, but also to the various regional actors that are part of its network. Initiatives such as sector-focused acceleration programs (e.g. on Green or Creative sectors), opportunities to go global (through, for instance, the partnership with the Tech Venture Launch Program in Silicon Valley) and planning of new services through the participation in EU projects, represent the key added value ERSU can offer to its community of users.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.047
GPT teacher head0.252
Teacher spread0.205 · 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