Grassroots innovations and innovators: the case of Iran
Bibliographic record
Abstract
Grassroots innovations (GIs) are considered alternative pathways of innovation that better match sustainable development goals. The study aims to better understand the nature of GIs in the context of a developing country, Iran. It introduces a novel dataset of GIs in Iran. It employs a descriptive analysis and K-mean cluster analysis on Multiple correspondence analysis (MCA) to investigate (1) the state of the art of GIs in the country, and if any patterns can be detected in the characteristics of GIs. The result indicates the majority of Iranian grassroots innovators are males living in urban areas. The provinces Fars and South-Khorasan accommodate together about 40% of the innovators, while Tehran (Iran’s capital) is ranked fourth after Hamadan Province. The study highlights the differences between the country’s GI and patent activities. The result of clustering analysis on MCA indicates the patterns of grassroots innovation in the context of Iran by detecting three clusters.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".