Bibliographic record
Abstract
In March 2023, third-year Technical Theatre (Production and Design) students at the University of Salford produced a festival of environmentally engaged design-led performance pieces focused on the climate crisis. All four productions were based on plays from Lighting the Way: An Anthology of Short Plays about the Climate Crisis (Bilodeau and Peterson 2020) and were devised and developed by the students, who were encouraged to be as sustainable as possible in both their designs and their execution. The festival was part of the first stage of our collaborative online international learning (COIL) project with Humber College in Toronto, which continued into 2024. In devising the projects, students were encouraged to consider concepts of affectivity, relationality and materiality in relation to expanded scenography (McKinney and Palmer 2017), the practice of scenography as a political act (Elnile 2020), and the ethics of ecoscenography (Beer n.d.). Using principles from the ABTT Green Book for sustainable productions (2021), all materials and equipment used in the final performances were either drawn from in-house stock or borrowed, repurposed or upcycled, encouraging further conversations around accessibility and our collective carbon footprint.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".