Community-based learning: A model for higher education and community partnerships
Bibliographic record
Abstract
This paper presents a model of community-based learning partnerships, developed at the University of Brighton, for consideration by Higher Education as a means to securing effective community informatics engagement. The absence of funding and time to pursue research proposals required me to be creative in continuing collaboration with our community partners of funded research projects. It is suggested here that the academic curriculum together with the resources and goodwill of a UK university can support both the formal requirements of HE student learning and the more informal learning needs of community practice through the development of community media/informatics learning partnerships. This is the first in a series of papers to be written that share the story of community-based learning experiences at the University of Brighton. Our purpose is to engage in meaningful community Informatics/media research and practice partnerships with a view to contributing to knowledge whilst affecting social change. A number of preliminary community informatics/media partnership activities are introduced through the joint lenses of community empowerment and community development. The significance of community voice and community learning in facilitating and enabling active citizenship and empowered communities through community informatics practices is also explored.
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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 | 0.025 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.006 |
| 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".