Standards for Evaluating Source Reliability and Information Credibility in Intelligence Production
Bibliographic record
Abstract
Intelligence practitioners must regularly exploit information of uncertain quality to support decision-making. Recognizing information evaluation as a key function within the intelligence process, some organizations provide standards for assessing and communicating relevant information characteristics. Despite their intent, however, many of these standards are inconsistent across organizations, and may be fundamentally flawed or otherwise ill-suited to the context of application. In certain situations, poorly formulated standards may actually inhibit collaboration, degrade the quality of analytic judgements, and impair decision-making. In order to develop evidence-based recommendations for future practice in the assessment and communication of information quality, SAS-114 collected standards in use across a variety of agencies and domains. The following chapter provides a critical examination of standards for evaluating source reliability and information credibility, and highlights avenues for future research and development.
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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 | 0.514 | 0.743 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.039 | 0.028 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".