Bibliographic record
Abstract
This project will be an examination of the potentialities of using conceptual blending to describe the cognitive processing that occurs when audience members engage in a theatrical event. Specifically, it will frame the processes using the play White Rabbit Red Rabbit by Nassim Soleimanpour, which itself examines the multiplicities of mental spaces required to engage in performance. In this project, I hope to examine conceptual blending and its relation to theatre, especially metatheatre, in which audience members must balance several levels of performance and reality in one theatrical event. There has been research conducted into relating blending theories with semantics, semiotics, and literature, in particular in the realm of metaphor in which a reader must maintain both the original and analogy in the same mental space in order to draw the comparison. The move towards theatre follows logically, as it encourages audiences to view a performance of fiction or imagination while balancing the 'real' quality of the actors, set pieces, or even words and story, as in verbatim and documentary performance, respectively. Considering these ideas, my core questions can be grouped around three main ideas: How does conceptual blending function when watching theatrical performance, specifically White Rabbit Red Rabbit? What specific moments in the script, performance, or audience experience in White Rabbit Red Rabbit prompt conceptual blending, or challenge our usual conceptual blending process? What implications are there for the use of conceptual blending or cognitive science in theatre for shaping audience perception?
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".