Clean Technology Transfer and Innovation in Social Housing Production in Brazil and Colombia. A Framework from a Systematic Review
Bibliographic record
Abstract
Over recent years in Brazil and Colombia, the social housing programs of these two countries have increasingly become directly related to the concept of green construction and seek to integrate with their respective laws. For example, a series of technological strategies allows bought countries to guarantee a reduction of the environmental impact of traditional construction technologies. Therefore, these actions try to answer the problems in the design of dwellings in Latin America. However, the construction sector reduced productivity and limited innovation in business. Some of the technological management processes in the social housing construction sector only consider the implementation of clean technologies tangentially. This situation is mainly because of general policies since they do not consider different local contexts. It is then worth asking: What impact do the processes of transfer of clean technologies have on social housing in Brazil and Colombia? This systematic review was carried out on scientific papers indexed by the science database from 2013 to 2019. The PRISMA method was applied to this review with an aim to propose a conceptual model for the transfer of clean technology in the production of social housing in Brazil and Colombia. Finally, we identify that the impact of clean technologies transfer on social housing is very low in these two countries.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".