Fire Science Myths: Examining Arson and Wrongful Convictions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Prior to 1992, fire investigators examined fire scenes through subjective observation and by process of elimination. If no cause could be determined for fire, arson would be assumed. If the cause were suspected, analysis of scenes based on an array of fire origin myths and patterns, such as crazed glass, would take place, usually resulting in a decision of intentional lighting. This research examines the use of fire science myths and fire pattern analysis in Canadian Courts, pointing to the potential for existing wrongful convictions based on outdated fire scene investigation methods. Through a mixed-methods study design, a sample of 30 court case summaries mentioning fire patterns were analyzed. Ten of which, dating prior and twenty occurring after 1992. As this research is in progress, results have not yet been formulated. However, speculation of current findings suggests that fire science myths have been used in Canadian court history.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it