A Global Perspective of Rural Innovation and Entrepreneurship in the Digital Era: A Panel Report
Bibliographic record
Abstract
Rural communities play a significant role in terms of agriculture, climate change, ecological balance, tourism, and indigenous cultures. However, traditionally, rural communities underperform in innovations and entrepreneurship, largely due to the lack of resources, infrastructure and various inhibiting cultural and political factors. With the advent of digital technologies, rural communities have received unique opportunities to engage in innovation and entrepreneurial activities that are affordable, easy-to-use, easy-to-learn, and easy-to-implement – bypassing some of the inherent challenges indigenous to rural areas. Considering the importance of this timely topic, a panel was conducted at the Pacific Asian Conference on Information Systems (PACIS) in Dubai in 2021. The objective of this panel was to initiate a much-needed conversation regarding rural innovation and entrepreneurship in the digital era and motivate academics, particularly information systems researchers, to conduct research to understand the role of digital technology in rural innovation and entrepreneurship. The panel report provides an overarching framework, that is based on socio-materialism, to guide future research in this emerging area of studies.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".