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Record W4244639189 · doi:10.1787/9789264213951-5-en

SME and entrepreneurship issues and policies in Italy: Assessment and recommendations

2014· book-chapter· en· W4244639189 on OpenAlexaboutno aff

Bibliographic record

VenueOECD studies on SMEs and entrepreneurship · 2014
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipQuarter (Canadian coin)BusinessEuropean unionInvestment (military)PopulationValue (mathematics)Eu countriesInternational tradeGeographyPolitical scienceFinance

Abstract

fetched live from OpenAlex

SMEs and entrepreneurs are the backbone of the Italian economy. With nearly 100 enterprises per thousand people its business density is one of the highest among OECD countries. One-quarter of the working population is self-employed, the second-highest rate in the European Union (EU). Italian SMEs contribute 80% of national employment and 67% of value added, the third and fifth highest shares in the OECD area respectively. SMEs are also at the core of Italy’s export and innovation performance, accounting for over 50% of the total volume of exports and 22% of business R&D investment. The share of firms that are young, with less than three years of age (14% of Italian enterprises), is in line with the most entrepreneurial OECD economies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.081
GPT teacher head0.309
Teacher spread0.228 · 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.

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

Citations2
Published2014
Admission routes1
Has abstractyes

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