Bibliographic record
Abstract
This thesis concerns ghost lights, a piece of lighting equipment often used in theatres, commonly at the discretion of the theatrical stage technicians who work behind the scenes. For this reason, this work centres the voices of stage technicians, engaging their perspectives through ethnographic research carried out at professional theatres in English-speaking Canada. Supported by a variety of folkloristic literature and material from the field of performance studies, this thesis explores ghost lights through a variety of lenses; first as occupational folklore, where creating and using a ghost light offers technicians a way to prove membership to the folk group and their own occupational competence. Second, this thesis explores what ghost lights can tell us about occupational folk belief, and how this contributes to the theatre folk group's understanding of community. Finally, this thesis analyzes ghost lights as vernacular expressions of grief and solidarity during Covid-19. Shared through images on social media, ghost lights were a way of engaging creativity to provide community care during global crisis. Through this exploration, I argue that ghost lights offer a mechanism to simultaneously make and mark (Moore and Myerhoff 1977) members of the technical theatre folk group, providing an avenue to perform both professional and creative community care.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.030 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".