Bibliographic record
Abstract
Live projects adopt a wide range of approaches: design/ build, community engagement, participation, protest, analysis, etc. They are driven by tutors with passion, expertise and the ability to manage risk, in ways that exhibit fluency and high levels of skill. They also offer sites of student-led and community co-learning, can support research, demonstrate ‘impact’ and satisfy universities’ policies on outreach. Whilst the breadth and reach of Live Projects is now demonstrably wide, we still fail to fully locate Live Projects within a pedagogical context, tending instead to limit our descriptions and hence analysis to the architectural process and outcome. This failure to locate Live Projects within a pedagogical framework means we still struggle to encapsulate, critique, progress, and indeed, elevate the work. This chapter draws on some of the case studies presented in recent papers and international conferences in order to provide educators with signposts and important overviews around which and in respect to they can develop their own pedagogical frameworks.
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 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 | 0.027 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".