Meditation in Two Parts and One Aphorism: Personal Experience, State Dirigisme, and New Play Development
Bibliographic record
Abstract
Through my years as a theatre practitioner, I have scarcely acknowledged and almost never identified with the institutions that funded or supported my endeavours. I simply took them for granted (pun intended). In fact, institutions were about the farthest thing from my mind, preoccupied as I was with the personal, artistic, and business challenges of making theatre and films. My focus, therefore, is on the relationships between and among creators and institutions involved in new play creation, production, and financing, on the one hand, and on the role of institutions in shaping Canadian identity and culture as a whole through organizational policy and action (including funding), on the other. Two problems framed as questions constitute a through line in this discourse. First, what are the possibilities of and limitations on institutional efficacy in achieving (national or individual) artistic and cultural policy objectives? Second, what is the relative importance of conscious intent in social and cultural policymaking to unconscious forces whether personal, collective, or institutional, that underlie creativity and affect sense of identity in the context of a Global North, elected democracy?
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.040 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".