Bibliographic record
Abstract
A renewed interest, propelled by the European Bauhaus initiative, has sparked a re-evaluation of design education in response to the growing complexity and interdisciplinary demands of design, encompassing both craftsmanship and academic discipline. While ongoing discussions focus on school types, curriculum development, and pedagogical approaches, there is an oversight in examining the expertise profiles of design educators. These profiles encapsulate the competencies and proficiencies of teaching staff, profoundly influencing the ethos, objectives, philosophy, and substance of education institutions. This paper proposes a theoretical framework delineating three archetypal expertise profiles for design educators: design practitioner, design researcher, and hybrid, nuanced to reflect the multifaceted nature of design expertise. Drawing insights from design history, theory, and professional experience, this framework holds promise in guiding the cultivation of expertise profiles, prioritizing proficiency enhancement, curation, and recognizing the value of hybrid profiles. Our aspiration is to elevate the quality, relevance, and adaptability of design education amidst the evolving landscape of contemporary design.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| 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".