PRME SIP 2.0: enhancing quality and maximising stakeholder value
Bibliographic record
Abstract
Producing and submitting a Sharing Information on Progress (SIP) report represents one of the key commitments of any PRME signatory, as well as a helpful way to inform and engage stakeholders, including faculty, students, industry partners, and accrediting bodies. In March 2024, PRME launched the ‘PRME Commons’, a multilevel technological platform to facilitate global knowledge exchange across business schools, which includes a self-reporting database (‘SIP 2.0’). Representing a significant shift from ‘SIP 1.0’, Signatories will now be required to answer nine questions aligned with the Seven PRME Principles and upload supporting evidence including objects (statements, policies, public media, etc.) and narratives (complementary context and/or stories). This interactive workshop will (1) address shared challenges and opportunities in relation to the transition to SIP 2.0; and (2) generate actionable ideas for how we can use SIP 2.0 and the PRME Commons to enhance value for organizational stakeholders. It will consist of a mix of short, focused breakout sessions and plenary discussions. The overarching aim is to increase participants clarity and confidence in terms of producing their next SIP report, as well as support them in maximising the value–and minimizing the burden–associated with reporting. Information and ideas generated through the session will be shared with the wider PRME UK and Ireland community afterwards via a blog post.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".