Proceedings of the RAISE International Colloquium on Partnership
Bibliographic record
Abstract
RAISE convened a major event on June 23 rd 2017, hosted at Birmingham City University. This was undertaken under the auspices of the RAISE Special Interest Group on Partnership. The event organisers were successful in bringing together leading, international commentators and practitioners to discuss and reflect on developments in partnerships between students and staff in Higher Education. We noted that students and staff working in partnership has rapidly become a major feature of the HE landscape around the world. There is much evidence to show that partnership working may be a powerful catalyst to enhance student engagement and enhance student learning. Indubitably there are benefits to staff and institutions too. Developing such an ethos presents an attractive alternative to neo-liberal, transactional and consumer models of HE. We wished to take stock of these developments and explore the opportunities, challenges, and consequences of such approaches. Is partnership truly inclusive and open to all? What are the ethical tensions? Are some of these practices more ‘pseudo-partnership’ then genuine? Is there a danger of appropriation through neo-liberal or managerialist agendas? We asked contributors to summarise the presentations and workshops they gave at the event for these proceedings and we are delighted that so many of them have been able to do so
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.082 | 0.016 |
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".