Influence of External Actors in Swedish Homeowners' Adoption of Energy Efficient Windows
Bibliographic record
Abstract
In Sweden, the transition of the society from agricultural to industrial occupation caused millions of people to move from the country-side into cities and in the 1960-ies and 1970-ies a broad building construction program was performed in order to build 1 million new dwellings in Sweden.However, these buildings are now after 40 years under urgent need of refurbishment and therefore offer a great opportunity for being supplied with modern and efficient construction details and heating systems.An example of such a project is the refurbishment of residential buildings in the quarter Brogården of Alingsås, were 16 buildings with 300 dwellings are to be converted from 1970-standards to modern passive house standards.The housing company Alingsåshem has in partnership with the construction company Skanska and under the consultancy of efem architects and the local Passive House Centrum started a refurbishment project for Brogården.The project involves the extensive renovation of the buildings with passive house techniques, and includes the installation of new façades and roofs, thicker insulation and new ventilation systems.The refurbished buildings do not use conventional heating systems and require very little energy for space heating.Hot water is primarily produced by solar energy, peak load energy is supplied by district heating,
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".