The role of emerging technologies in inclusive innovation: the case of nanotechnology in South Africa†
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
Inclusive innovation seeks to understand how new technologies can provide benefits for the poor and marginalized. However, the role emerging technologies might play in inclusive innovation is not well understood. We synthesize inclusive innovation frameworks with work on equity and emerging technologies by examining the South African National Nanotechnology Strategy. Through document analysis and interviews, we find that some nanotechnology projects address problems of poor communities. The future nanotechnology workforce also reflects South Africa’s diversity. Nanotechnology research supports existing businesses and has helped create some new firms, which might increase employment. Overall, the effort created nanotechnology innovation that is somewhat inclusive in its intent, impact, process, and structure. However, innovation could be more inclusive and just by focusing more on technology transfer systems for disadvantaged communities and on rural development. The case illustrates that combining inclusive innovation and equity frameworks provides more specific policy pathways and deeper understanding of justice.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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