Replication vs mentoring: Accelerating the spread of good practices for the low-carbon transition
Bibliographic record
Abstract
The challenge of making cities more sustainable is one of the major constraints that has to be addressed at all political levels.Many innovative planning solutions are now underway in various European cities of any scale.One way of making the transition to low-carbon cities happen is the approach of replicating successful demonstration projects.During several years of participatory observation in European projects and municipal consultancy as well as through qualitative interviews with municipal technical staff working on climate change, we observed that replication is seen by the European Commission as well as national governments as a major solution for speeding up the transition EU wide.The research includes an evaluation of already funded EU projects using a replication approach.It is commonplace that replication is not likely to happen 1:1, because each city has its own challenges.Nonetheless, the process behind replication attempts leads to considerable learning effects.We found out that learning from good examples serves several purposes for managing the transition, e.g.inspiration and motivation of technical staff, mobilisation of stakeholders or political commitment.The paper concludes with an analysis of success factors and barriers for replication drawing on real life examples.The findings recommend making supporting schemes more effective by evolving the concept of unstructured replication towards a mentoring approach based on scientific steering.
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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 | 0.121 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".