Learning Informally
Bibliographic record
Abstract
This paper advocates for the inclusion of the arts in vocational learning programs in Uganda, as an integrated form of holistic learning oriented towards empowerment and entrepreneurship. Using community-based research in the context of vocational education and training (VET), our data emerged from open-ended interviews, focus groups and youth-led radio talk shows with stakeholders from public and private sectors, instructors, artists, NGO’s. Three significant themes arose from the data collected. Firstly, the pathways available to learners to become artists are limited by neoliberal and technicist orientations towards education. Secondly, there are significant potential opportunities and interests. Secondly, there is a thriving informal youth-led arts community in northern Uganda, which thirdly, without ways for learners to generate income, they are not able to devote their time to learning through the arts, and their artistic endeavours are not recognized as important skills in their communities or in society. We demonstrate that there is a vibrant space in the informal sector of arts, that if supported could become important and much needed sectors Uganda.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.025 |
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".