Entrepreneurship in India's Handicraft Industry with the Support of Digital Technology and Innovation During Natural Calamities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research aimed to identify the characteristics that either foster or stifle digital innovation and entrepreneurship amongst small businesses operating in the Handicraft industry during times of economic downturn. In the eyes of young Indian craft entrepreneurs, digital technology is essential for surviving the crisis and would help, for the most part, the artisanal and handmade goods market and the entrepreneurial spirit. Fifty owners of online handicraft businesses, all of whom held unique craft skills, were interviewed using a qualitative technique, and the researcher then utilized inductive (qualitative) content analysis to draw out common threads from the transcripts. The findings showed that the Pandemic's internal and external factors encourage the movement of handicraft businesses to digital platforms, fostering entrepreneurship and digital innovation. The respondents identified several obstacles, including a lack of available high-quality digital infrastructures, the spread of pandemics, market worries over digital platforms, and the lack of knowledge and IT skills required to run an online business. The article's findings contribute to the growing body of digital information on novel approaches to entrepreneurship and suggest avenues for carrying out quantitative research toward the end of creating aid programmes for proprietors of handmade goods enterprises during economic downturns. This could serve as a standard against which new policies and tactics for reviving the economy and expanding the handmade goods industry through technological and entrepreneurial ingenuity can be measured.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it