Bibliographic record
Abstract
Chad Harbach’s 2014 essay “MFA vs. NYC” draws a map of the creative writing universe with two polestars: either ongoing engagement with university writing programs or an orbit around the publishing world itself. As enticing as both those worlds might be, neither of these models capture this professor’s own entry into the writing life. His last academic degree is an MBA, and his last full-time employer before UBC was a bank. Given, as well, his history of freelance writing, this professor in Canada’s oldest and largest CW program teaches more MBA than MFA or NYC. Prof. Taylor has undertaken various steps in his academic career to both capture this thinker-for-hire mindset in his own work and offer it to students. He is currently Artist in Residence at UBC’s Quantum Matter Institute, writing a book for publication by UBC’s Belkin Gallery next year. He teaches a course combining MFA candidates and those pursuing PhDs in quantum physics. This chapter unpacks the pedagogical logic of this interdisciplinary approach, the wide-reaching way that it serves to tap students into different disciplines, and the ways in which he has seen this approach yield discoveries, new opportunities for students and a broader sense of the writing horizon.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".