Bibliographic record
Abstract
When a theatrical performance is digitally broadcast live to cinemas, the limitations of temporal and spatial specificity are removed and the theatrical experience is simultaneously opened up to a wider audience and inherently altered. One such production, Coriolanus (Donmar Warehouse, 2013-14), starring an actor with a particularly enthusiastic online fan community, was broadcast to cinemas by National Theatre Live, where fans recorded it on digital devices, extracted clips and produced animated gifs, which they captioned to reinterpret the play, sharing them online, removed from their original context. The transformation of theatre texts to cinemas to social media platforms raises exciting questions related to how fans interact with culture both as consumers and as producers of new media texts. How do the different transformations (technical and actively fan-produced) affect both the narrative and the cultural experience? How do new texts function as surrogates for, and extensions of, the ‘official' narrative, as well as new interactive narratives in their own right? This paper addresses these questions in the context of a specific theatrical event as it crossed the boundary from a live, co-located experience first into cinema, and then into interactive hypertexts and memes. Drawing on theories of fandom and participatory culture, as well as post-Web 2.0 analyses of Internet behaviours, the paper examines fan production of new media texts and how they both transmit and transform the source narrative via interpretation, re-interpretation, and misinterpretation. Image Credit: Still of fromhiddleswithlove (2014)
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".