Meta‐informational cue inconsistency and judgment of information accuracy: Spotlight on intelligence analysis
Bibliographic record
Abstract
Abstract Meta‐information is information about information that can be used as cues to guide judgments and decisions. Three types of meta‐information that are routinely used in intelligence analysis are source reliability, information credibility, and classification level. The first two cues are intended to speak to information quality (in particular, the probability that the information is accurate), and classification level is intended to describe the information's security sensitivity. Two experiments involving professional intelligence analysts ( N = 25 and 27, respectively) manipulated meta‐information in a 6 (source reliability) × 6 (information credibility) × 2 (classification) repeated‐measures design. Ten additional items were retested to measure intra‐individual reliability. Analysts judged the probability of information accuracy based on its meta‐informational profile. In both experiments, the judged probability of information accuracy was sensitive to ordinal position on the scales and the directionality of linguistic terms used to anchor the levels of the two scales. Directionality led analysts to group the first three levels of each scale in a positive group and the fourth and fifth levels in a negative group, with the neutral term “cannot be judged” falling between these groups. Critically, as reliability and credibility cue inconsistency increased, there was a corresponding decrease in intra‐analyst reliability, interanalyst agreement, and effective cue utilization. Neither experiment found a significant effect of classification on probability judgments.
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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 | 0.039 | 0.324 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".