Liverpool knowledge quarter sustainability network: case study
Bibliographic record
Abstract
Purpose This paper aims to study the development of the Liverpool Knowledge Quarter Sustainability Network (KQSN). It outlines the sectors included in the collaborative knowledge-sharing, the nature of the work it facilitates, and considers how the network can transform its existing objectives around the shared vision of the United Nations sustainable development goals (SDGs). Design/methodology/approach The KQSN operates in a collaborative cross-sectoral forum to support, facilitate or coordinate projects around sustainability, with core leads sitting in higher education and health care. Findings The KQSN supports projects through collaborative activity and enables members to access specialist advice available through the network. Through its membership, the KQSN is primed to develop metrics for demonstrating Knowledge Quarter SDG-aligned activity. The KQSN has scope to increase its level of implementation arising from its shared values, with a renewed focus around the SDGs. Practical implications This paper contributes to the 2018 EAUC Annual Conference theme of “Collaborations for Change” and the need for transformative partnerships that are prepared to align their mission to the SDGs. Originality/value Unlike discipline- or sector-specific networks, the KQSN has an inclusive membership, making it an original multi-disciplinary sustainability platform for neighbouring organisations in and around Liverpool's Knowledge Quarter. This case study can support other knowledge cluster communities to replicate its model. This case study also presents a diverse range of small projects, which are easily replicable and hopefully will inspire others to do something similar.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".