Bibliographic record
Abstract
This conversation with award-winning Canadian designer Shawn Kerwin explores the need for emerging designers to embrace risk and create their own opportunities for learning and professional development. Kerwin has designed sets and costumes for theatre for over forty-five years and has taught at the post-secondary level for over twenty-five. Her design work has been on stages across North America and across the world. While she holds a passion for opera and the classic repertoire, she has also been involved in numerous world premieres of plays and opera. As a teacher, she is passionate about helping students find their own creative voice and introducing them to the work of Canadian playwrights, many of whom she has had the pleasure of working with on new scripts. Over the past two years, in collaboration with the Charlottetown Festival and other organizations, she has mentored numerous young designers as they make the transition from being a student to becoming an active professional. She is currently exploring virtual costume design in an ongoing effort to continue learning and building her own skills.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".