The effect of source reliability and information credibility on judgments of information quality in intelligence analysis
Bibliographic record
Abstract
Abstract The quality of information that informs decisions in expert domains such as law enforcement and national security often requires assessment based on meta-informational attributes such as source reliability and information credibility. Across 2 experiments with intelligence analysts ( n = 74) and nonexperts ( n = 175), participants rated the accuracy, informativeness, trustworthiness, and usefulness of information varying in source reliability and information credibility. The latter 2 attributes were communicated using ratings from the Admiralty Code, an information-evaluation system widely used in the defence and security domain since the 1940s. Ratings of accuracy, informativeness, and likelihood of use were elicited as repeated measures to examine intraindividual reliability. Across experiments, intraindividual reliability was best when levels of source reliability and information credibility were moderately consistent compared to when they were maximally inconsistent (i.e., one low and one high) or maximally consistent (both high or low). As well, trustworthiness ratings depended more on source reliability than on information credibility. Finally, the likelihood of using information was consistently predicted by accuracy ratings and not by judged informativeness or trustworthiness. The current findings offer insights into the ability of experts and novices to reliably use information-evaluation systems for structuring human judgments about intelligence.
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 | 0.036 | 0.378 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".