Petri Dish Deceptions: A Search for 100% Veracity in Rimini Protokoll’s Statistical Portrait of Montreal
Bibliographic record
Abstract
In the wake of 2016—the year of ‘post-truth’ and ‘fake facts’—Rimini Protokoll’s participatory theatre work, 100% Montréal, attempted to reveal what the city’s census data had failed to capture about the lives of its citizens. This article examines how their theatrical juxtaposition of a city’s demographic data with the onstage testimonies of 100 residents reveals important underlying factors that have plagued the pursuit of accuracy in census-taking throughout history. In doing so, 100% Montreal effectively stages the conditions under which contemporary definitions of ‘truth’ become increasingly mutable and elusive, while underscoring the extent to which the public disclosure of accurate and truthful testimony relies on a trustworthy culture of respect and privacy protection between the surveyors and the surveyed.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".