Bibliographic record
Abstract
Focusing on the activities of LARGE (the Live Action Role-Playing Guild of Edmonton), several performance concepts are explored using the primary sources of five interview subjects and the author’s auto-ethnographic perspective on participating in LARPs in the Edmonton area. Some of these performance concepts are how LARP can be considered “improv in disguise,” how the use of rules systems both helps and hinders ideas of agency and immersion, and how issues of consent are essential to the success of creating a shared reality among diverse personalities. The article begins with an auto-ethnographic description of the LARP event Broken Covenant, which leads to explaining how LARGE began and how it differs from more well-known boffer LARPS. The article then drills down on the varied histories of the interview subjects and explores the concept of improvisation in LARP as ‘yes, but…,’ in comparison to the well-known improv mantra of ‘yes, and. …’ Agency and immersion are explored, leading to a discussion of emotional investment and, most important, consent. The article demonstrates that there is a vibrant and sophisticated LARP community in Edmonton (mostly transplanted from Saskatoon) creating performances in the non-traditional milieu of LARP.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".