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Record W4319730343 · doi:10.18235/0004733

Santo Domingo, República Dominicana: mapeo del ecosistema de emprendimiento con foco en GovTech

2023· report· es· W4319730343 on OpenAlexaff
Carlos Acuña, Jimena Aucique, Rocío Suanzes, Javier Solana Madariaga, Natalia Laguyás, Smeldy Ramirez Rufino, Sina del Rosario

Bibliographic record

Venuenot available
Typereport
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsOptech (Canada)
Fundersnot available
KeywordsHumanitiesPolitical scienceGeographyArt

Abstract

fetched live from OpenAlex

Este análisis del ecosistema de emprendimiento innovador en Santo Domingo, República Dominicana desde la óptica GovTech se realizó con miras a identificar los diversos actores que, por su área de especialización (verticales de trabajo) o su actividad, deberían conectarse para permitir que más soluciones lleguen del sector privado al sector público para mejorar los servicios que se prestan a la ciudadanía. De todos los actores se analizaron dos variables: el grado de alcance de la institución y el Grado de relación GovTech. Los actores se identificaron en el mapeo se clasifican según su rol en el ecosistema de la siguiente manera: Sector Público, Sector Privado, Inversión/Financiamiento, Academia y Emprendimiento.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.520
Threshold uncertainty score0.966

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.094
GPT teacher head0.297
Teacher spread0.203 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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