An Investigation of the contribution of 4IR to Competencies of Architects
Bibliographic record
Abstract
This article aims to investigate the role/contribution of 4IR to competencies of architects in the pre-construction project development planning in South Africa.The study will explore the knowledge, skills, and abilities required by architects during the early stages of project development, focusing on their ability to conceptualize, design, and plan projects to meet client requirements, adhere to regulatory standards, and achieve sustainability goals.The research will include an overview of the potential application of 4IR technologies to enhance the competencies of architects as well as a comprehensive literature review to identify the existing body of knowledge on this topic, followed by a quantitative survey to collect data from architects working across various architectural firms in South Africa.The findings will provide insights into the current competencies of architects in pre-construction project development planning, identify gaps in their skills, and suggest recommendations for architectural education and professional development programs in South Africa.Ultimately, this research aims to contribute to the enhancement of architectural practice and the overall efficiency and sustainability of the construction industry in South Africa.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".