Examine an Intelligence Education Framework of Landscape Architecture (EFLA) Based on Network Model of Technology in Landscape Architecture (NMTLA)
Bibliographic record
Abstract
The discipline of Landscape Architecture (LA) is currently expanding its disciplinary boundary. The supporting Technology in LA (TLA) is always evolving and optimized to solve environmental problems. Considering the uncertain classification of the current LA knowledge for education and the importance of technology in LA education, a refined education framework of LA is needed. This research first established a Network Model of Technology in LA (NMTLA) using Network Analysis (NA) and expert interviews. Then, this research proposed an Education Framework of LA (EFLA) based on the NMTLA. To build the NMTLA, this research identified 23 key categories of TLA through content analysis of secondary research. Then, the expert interview and network theory were used to analyze and visualize the relationships among the categories. By examining the degree centrality, closeness centrality, and betweenness centrality of different TLA, this study developed an EFLA which summarizes the twenty-three categories of TLA into four domains: core techniques, applied technologies, integrated technologies, and specific technologies. This study also proposes a series of suggestions for how to apply different categories of TLA in today’s and future LA education. The proposed NMTLA and EFLA in this research can contribute to the development of future LA higher education. They also can potentially address the Sustainable Development Goals (SDGs) in LA education and industry. However, the scope of this study is currently limited to LA education in the USA, which could be expanded to include a worldwide perspective in future research. To enhance the validity of the conclusions, a larger sample size for interviews should be employed in further studies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".