A Framework for an Integrated and Evolutionary Body of Knowledge
Bibliographic record
Abstract
Construction-related practices are facing enormous pressure for change because of low productivity, the lack of sustainability and the often poor quality of delivered buildings. New methods and tools have been proposed to address the problems associated with the fragmentation of tasks, disciplines and responsibilities that contribute to the industry's poor performance. However, there is still no unified proposition to guide the industry in rethinking and integrating their practices. There is obviously a need for a framework that combines work processes, technological means, normative aspects and domain knowledge. This paper proposes a model for such a framework; a combination of the Integrated Design Process (IDP) and Building Information Modeling (BIM) for sustainable built environment. Its theoretical background draws from studies in social learning (activity theory and situated action theories). These theories suggest that learning and knowledge generation occur mainly within a social process, defined as an activity. This corresponds to the context in which the IDP-BIM framework would be used, as its final objective is the transformation of building design practices. Two validation scenarios are under development and observation: one evaluates how the Framework helps to create a common language among different building specialists, and the other assesses the ergonomics of the digital interface.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".