Diffusion of cleaner production innovation in clay-fired brick sector - case study of Varanasi brick cluster in Eastern India
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".