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Inovação tecnológica como agente de redução de impactos ambientais da indústria de rochas ornamentais no estado do Rio de Janeiro

2013· article· pt· W1996694645 on OpenAlexaff
Romeu e Silva Neto, Bruno dos Santos Silvestre

Bibliographic record

VenueAmbiente Construído · 2013
Typearticle
Languagept
FieldDecision Sciences
TopicBusiness and Management Studies
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

A indústria de rochas ornamentais da região noroeste fluminense constitui um arranjo produtivo local de grande importância econômica. Entretanto, as empresas fazem uso de técnicas rudimentares em seus processos produtivos, o que causa sérios problemas ambientais e de competitividade. Cientes de que a principal dificuldade enfrentada por esta indústria está relacionada com a ausência de tecnologias, governo, universidades e organizações da sociedade civil têm tentado desenvolver e difundir tecnologias. O objetivo deste trabalho de pesquisa é identificar e descrever os fatores que impedem a difusão de tecnologia neste arranjo produtivo. Foram realizados múltiplos estudos de caso de caráter exploratório, descritivo e explicativo, nos quais foram utilizadas múltiplas fontes de evidência, tais como revisão bibliográfica, entrevistas semi-estruturadas com empresários e profissionais do setor, além de visitas técnicas a empresas locais. Os resultados apontam para dificuldades na difusão dessas tecnologias, especialmente para as pequenas empresas, tais como baixa qualificação de empresários e trabalhadores, resistência a mudanças dentro das empresas, e dificuldade de articulação, já que, atualmente, existe disponibilidade de recursos financeiros e apoio institucional para a inovação.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.058
GPT teacher head0.334
Teacher spread0.276 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations0
Published2013
Admission routes1
Has abstractyes

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