Reporting with purpose? Preliminary findings from a study of the first year of PRME SIP 2.0
Bibliographic record
Abstract
The Principles for Responsible Management Education (PRME) is a United Nations (UN) supported initiative established under the aegis of the UN Global Compact to raise the profile of sustainability in business and management education. PRME is underpinned by seven Principles that seek to guide business and management schools towards the holistic integration of ethics, social responsibility, and sustainability into their mission, vision, strategy, curricula, research, partnerships, and operations. Sharing Information on Progress (SIP) reporting is a key commitment for all PRME Signatories, as well as an important vehicle for engaging institutional stakeholders. In 2024, PRME introduced a revised reporting framework (‘SIP 2.0’), as well as a new reporting platform, the PRME Commons. The aim of these changes was to improve transparency and accountability, enhance learning, promote dialogue, capture impact, strengthen alignment with the Principles of PRME, and–not least–streamline the reporting process. This working paper presents the initial findings from a project analysing the content of over 160 SIP 2.0 reports submitted in 2024. It draws attention to the strengths of the new approach, highlights areas for development, and seeks to stimulate discussion on what makes for an ‘excellent’ SIP report according to different stakeholder perspectives.
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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 | 0.172 | 0.410 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".