A forensic linguistic Analysis of Discursive Deception in Criminals' Statements: A Case Study of the Canadian Serial Killer Robert Pickton
Bibliographic record
Abstract
This research paper aims at specifying the indicators of discursive deception in the interrogation of the most famous Canadian serial killer Robert Pickton on February 23, 2002. Qualitative and quantitative methods are used in this research. The qualitative method appears in the forensic linguistic analysis of the indicators of discursive deception. The approaches adopted for the analysis of Pickton's statement are Mack McClish's (2001) approach for statement analysis as represented in his I know You are Lying: Detecting Deception Through Statement Analysis and the approach of John H. Powers’ (2019) for discursive deception as represented in his essay “Discursive Dimensions of Deceptive Communication: a Framework for Practical Analysis”. The quantitative method appears in the numerical data using Laurence Anthony's AntConic Software version 3.5.8 (2019) and Mick O' Donnell's UAM Corpus tool version 3.3x 2007. The results of this study are deduced based on the frequency tables of the indicators of discursive deception used by Pickton in his police interrogation on February 23, 2002. This study suggests a classification of the indicators of criminals’ discursive deception based on analyzing the indicators of discursive deception exploited by Robert Pickton in his police interrogation on February 23, 2002. Pickton’s interrogation revolves around the accusations of fifty murder cases of sex-working girls in his pig farm in Port Coquitlam, British Columbia Canada. He is known as the butcher or the pig farmer killer.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".