Developing an Inclusive Definition, Typological Analysis and Online Resource for Live Projects
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
This chapter describes the experimentation with and documentation and analysis of a diverse range of Live Projects, discussed in relation to Lave and Wenger's theory of situated learning via a process of legitimate peripheral participation and findings from OB1 LIVE, the authors' programme of Live Projects. The projects demonstrate the reasoning behind the authors identification of six factors needed to make a project 'live'. From an initial analysis of the first fifteen different Live Project case studies on the network from Oxford Brookes University, McGill School of Architecture, Montreal, and the University of Portsmouth the authors saw that even very diverse project types shared the six factors that the authors had identified. In 2012 the authors established the Live Projects Network, an international online network of Live Projects to connect students, academics, practitioners and external collaborators involved in Live Projects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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