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Record W2619210870 · doi:10.1504/ijeim.2017.10005358

Diffusion of cleaner production innovation in clay-fired brick sector - case study of Varanasi brick cluster in Eastern India

2017· article· en· W2619210870 on OpenAlexaff
Nonita T. Yap, Sachin Kumar, Geeta Vaidyanathan, Prateek Sharma

Bibliographic record

VenueInternational Journal of Entrepreneurship and Innovation Management · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicIndian Economic and Social Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBrickProduction (economics)BusinessCluster (spacecraft)KilnEngineeringCivil engineeringEconomicsWaste managementComputer science

Abstract

fetched live from OpenAlex

The micro, small and medium enterprises sector is a highly vibrant and dynamic contributor to the Indian economy. Clay-fired brick making is a prominent MSME sub-sector in India that has not experienced any significant technological change. Zig-zag firing technology, a cleaner production initiative, introduced in the brick making process in the 1970s did not diffuse then, but is increasingly finding its way now after about four decades. This paper attempts to understand the factors that influence adoption of cleaner production innovation by MSME units, focusing on a specific brick making cluster in eastern India. Structured interviews were carried out in the field with 42 brick kiln owners, representing 18% of all brick kiln owners in the cluster. The results of the findings support the idea that external factors that help in creating an enabling environment and the characteristics of brick kiln entrepreneurs play an important role in the diffusion of new technology.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
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 head0.052
GPT teacher head0.276
Teacher spread0.224 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2017
Admission routes1
Has abstractyes

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