When data is limited or ambiguous, VidScore produces deliberately general results rather than guessing. Vagueness in the presence of uncertainty is more honest than false precision.
How uncertainty shows up
Results may use general language, appear sparse, or avoid definitive statements. This does not mean the system is hiding information - it means the available data does not support a more specific answer.
What to do
If uncertainty makes a result confusing, check the original content for more context. General results are not less valuable - they may correctly reflect that limited information is available.
Detailed reference
What you can expect
- Results that use general language rather than specific claims
- Sections that appear sparse or less detailed when data is limited
- Associations that are surfaced without strong emphasis when confidence is low
- Results that avoid definitive statements about identity, intent, or involvement
Limits
- Confidence scores or certainty ratings visible to users. The system does not expose internal confidence levels
- Guarantees that cautious results are accurate. They are still informational and may be wrong
- Explanations of why a specific result is more or less detailed than another
How it works
The system adjusts the specificity and prominence of results based on the strength and consistency of available signals. Strong, consistent signals across multiple sources produce more detailed, specific results. Weak, inconsistent, or sparse signals produce more general results.
This does not mean general results are less valuable. They may correctly indicate that only limited information is available. The system prefers to undersell uncertain information rather than oversell it.
Common misconceptions
- Interpreting vague results as evasive. They usually reflect limited data, not deliberate omission
- Assuming detailed results are more trustworthy than general ones. Both are informational and may be inaccurate
- Believing the system always knows more than it shows. In many cases, general results are the honest limit of available data
What to do
- Do not over-interpret general or vague results. They may indicate limited data rather than a problem
Last updated 2026-01-27