Assessing a Swedish Social Impact Assessment model for the construction Industry : A Case Study of the Development Project Järvalyftet
Bibliographic record
Abstract
The construction industry has an increased focus on using sustainable methods to reach a more sustainable society but is still lacking the social aspect of sustainability. The belief is that this aspect has to be a natural part in construction projects to successfully plan and develop sustainable societies. A method to achieve this could be the use of Social Impact Assessment (SIA), a method frequently used in other countries such as U.S, Australia and Canada. The aim with the thesis is to investigate how this method can be used in a Swedish context, but also how the public in the best manner can be involved in decisions that affect them. To complement the theory with empirical findings a case study is done within Järvalyftet, one of the biggest redevelopment projects in the Stockholm region at the moment. A SIA deals with several areas and issues and is therefore complex to perform in an efficient way. It is consequently important to create a team with mixed disciplines to be able to manage the work and face the different problems in the best possible way. The thesis indicates that public involvement is of major importance to create an acceptance for the planned project among the affected parties in order to reduce both the timeframe and the costs of the project. Further, the thesis indicates that public involvement early on in a project facilitates for the affected people to deal with changes and trade-offs resulting by the project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".