Who Will Teach the Next Generation of Landscape Architects? Ten-Year Review of Academic Position Descriptions in Landscape Architecture in North America
Bibliographic record
Abstract
Who will teach the next generation of landscape architects? It is not very often that we raise this question and study academic position openings in landscape architecture programs as an empirical inquiry for understanding the current state and future direction of landscape architecture. It is critical, however, to question the qualities sought by academia by academics to offer in-sight into educational, scholarly, and professional trends. The number and content of academic position openings recorded in landscape architecture programs offered an opportunity to conduct a content analysis that is essentially a snapshot of the state of landscape architecture. This research reviews landscape architecture academic position opening descriptions over a 10-year period from 2007 to 2016. It specifically focuses on data classification, content analysis, and synthesis of 314 tenured or tenure-track position descriptions in the United States and Canada. The article reports on the findings on topics such as the robust demands in academic or professional credentials preferred, specialized teaching and research subject areas desired, and the preparation needed to become an academic in landscape architecture. The results reveal increasing expectations in education, research, and professional qualifications and experience, adding to the complexity of being considered for a permanent academic position in landscape architecture. In short, research highlights the complex set of needs for a well-rounded candidate who can equally respond to scholarly aspirations and professional needs in landscape architecture to educate future educators, researchers, scholars, and practitioners.
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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".