Cheetah Generation: Youth Social Entrepreneurship in Nairobi
Bibliographic record
Abstract
From a young age, I have used artistic expressions to tackle social issues in Kenya and Canada, and witnessed how it can be a powerful tool in fostering social change. Whilst growing up in Kenya, I noticed a clear division between the haves and the have-nots. It was always disheartening to see those without and those struggling to make ends meet. I have always been passionate about poverty alleviation, but the question of social enterprises as a framework for poverty alleviation sparked my curiosity several years ago. This curiosity and seeking to merge my various interests was the reason I applied to the Strategic Foresight and Innovation program. I am interested in how storytelling, and ideas within development economics and design thinking can be employed within social enterprises to reduce poverty and create self-sustaining communities. My work with the United Nations, Ontario government, University of Guelph, Association for Canadian Educational Resources and Mennonite Economic Development Associates have led to some of the research questions and have significantly informed the work. I chose to focus on Kenya because it is a context that I am familiar with and would like to continue with this work in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".