Skills development for retrofitting a historic listed building in Scotland
Bibliographic record
Abstract
With the current aim for a low carbon economy in Scotland, it becomes imperative to ensure that there are adequate workforce skills available to support meeting this aspiration. As such, the Scottish Government has developed a low carbon skills agenda that emphasizes rapidly developing and delivering specialist skills that are needed to enable the adoption of new technologies. Developing and delivering specialist skills are arguably not possible without having an understanding of what these skills are. This paper thus reports on the successful trial of an innovative Canadian insulation technology in a historic listed building in Aberdeenshire with a particular emphasis on providing insights into workforce skills needs. The trial was funded by the Scottish Government and the European Regional Development Fund. An ‘insulation job’ worksheet is developed as a result of the project, which can aid effective project management of insulation jobs in the future. It is evident that the current skills in the industry could be made adaptable to the skills needs for insulating historic listed buildings. Multi-skilling [in particular for small–medium size enterprise (SMEs)] may become inevitable if the size of the project is small as it was the case with the project presented in this paper. Providing learning support for local SMEs, who are still building-up their capability in insulation technologies, is thus essential. Indeed knowledge sharing and dissemination of case studies for successful retrofitting (e.g. insulation) of buildings, in particular historic ones, can inform future development of ‘Low Carbon Skills’ policy and action.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".