An EDA Primer for Polygraph Examiners
Bibliographic record
Abstract
Of all the signals collected and analyzed during psychophysiological detection of deception (PDD) or polygraph testing, the electrodermal response (EDR) is the most robust and informative. The EDR is easily collected and is simple to measure and interpret (Blalock, Cushman, & Nelson, 2009). Several studies indicate the electrodermal component provides the greatest contribution to diagnostic accuracy in the comparison question test (Blalock, Cushman, & Nelson, 2009; Capps & Ansley, 1992; Harris & Olsen, 1994; Kircher & Raskin, 1988; Krapol & Handler, 2006; Krapohl & McManus, 1999; Nelson, Krapohl, & Handler, 2008; Raskin, Kircher, Honts, & Horowitz, 1988). The basic premise underlying the interpretations of EDRs is that the magnitude of response is commensurate with the degree of psychological importance that the examinee imparts to each stimulus question during testing. Peterson (1907), a student of the famous psychologist Carl Jung wrote: "It is like fishing in a sea of the unconscious, and the fish that likes the bait best jumps to the hook...Every stimulus accompanied by an emotion produced a deviation of the galvanometer to a degree of direct proportion to the liveliness and actuality of the emotion aroused" (p. 805).
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 | 0.064 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.088 | 0.063 |
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".