Telos Audience Purpose Alignment Checker
Audit independent purpose/audience alignment witnesses, not demographic profiling or persuasion prediction. Feature columns, independently annotated in order: purpose independently stated, audience goals independently established, purpose-goal fit independently witnessed. Unknown evidence and compatible classes retained; not automated semantic, historical, identity, legal or safety determination.
Description
Purpose-audience alignment concerns whether a declared purpose fits independently established audience goals.
Purpose-audience alignment concerns whether a declared purpose fits independently established audience goals.1
The checker keeps stated purpose, audience evidence, and witnessed fit as separate annotations.
When to use Telos Audience Purpose Alignment Checker
Use this tool when you need to audit explicit construction annotations while keeping unknown features visible. Establish the language, clause boundaries, transcription basis, source context, or evidence annotations required by this particular tool.
How to use Telos Audience Purpose Alignment Checker and understand its results
- Establish the language, clause boundaries, transcription basis, source context, or evidence annotations required by this particular tool.
- Enter observedFeatureFlags, featureKnownFlags, following the field table and calculation rules below, then run the tool.
- Read result. Inspect the nested fields in the output table below and keep any missing evidence or selected indices visible.
- Correct any input issues and run again. Before using a successful result, review its units, signs, evidence coverage, and stated model or reference assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| observedFeatureFlags | Binary presence/absence annotations in the fixed feature-column order | list of number | Required |
| featureKnownFlags | Binary evidence-known flags; zero leaves that feature unknown | list of number | Required |
Enter aligned binary observedFeatureFlags and featureKnownFlags in the exact fixed column order below. For each class, compare known features with its fixed rule: one requires presence, zero requires absence, and minus one ignores the column. A known mismatch excludes the class; an unknown required feature leaves it compatible but unwitnessed. Return all compatible classes, fully witnessed classes, and zero-based unknown-feature indices. A fully witnessed candidate satisfies only these annotations, not a broader semantic or intent test.
compatible_c=∀j(rule_cj=−1 or known_j=0 or feature_j=rule_cj); witnessed_c=∀j(rule_cj=−1 or (known_j=1 and feature_j=rule_cj)) Read these symbols with the field meanings and branch or reference rules above.
Fixed feature column order: purpose independently stated, audience goals independently established, purpose-goal fit independently witnessed.
| Candidate label | Fixed feature rule |
|---|---|
| caller-supported-alignment | [1,1,1] |
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the fields below | object |
| result.compatibleClasses | Candidates with no known feature contradiction; missing evidence may remain | list |
| result.witnessedClasses | Candidates with every required feature known and satisfied | list |
| result.unknownFeatureIndices | Zero-based feature columns whose known flag is zero | list |
{
"input": {
"observedFeatureFlags": [
1,
1,
1
],
"featureKnownFlags": [
1,
1,
1
]
},
"output": {
"result": {
"compatibleClasses": [
"caller-supported-alignment"
],
"witnessedClasses": [
"caller-supported-alignment"
],
"unknownFeatureIndices": []
}
}
}Limitations and assumptions
Annotations and their interpretation must be established independently. Multiple candidates may remain, including candidates with missing evidence. This does not parse raw language, identify intention, prove argument soundness, or infer a rhetorical effect.
Missing required fields, unknown fields, wrong types, and violated calculation rules return input issues. Numeric values must be finite; nonfinite numeric outputs are rejected. Arrays must be dense, with no missing cells. Numeric lists allow at most 10000 entries, subject to smaller tool-specific bounds above. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related task, use Audience Demographic Psychographic Grid: An audience grid can keep supplied segment identifiers and contextual notes aligned. Its labels do not establish sensitive traits, psychological predictions, or consent beyond what has independently been recorded. For a complementary step, use Kairos Timing Opportunity Window Lookup: Kairos concerns a situated opportunity relative to audience and circumstance. A clock reading alone cannot determine that rhetorical timing is appropriate.
References
-
Telos Audience Purpose Alignment Checker — tool.io
Similar or alternative tools
- Audience Demographic Psychographic Grid
An audience grid can keep supplied segment identifiers and contextual notes aligned. Its labels do not establish sensitive traits, psychological predictions, or consent beyond what has independently been recorded.
- Kairos Timing Opportunity Window Lookup
Kairos concerns a situated opportunity relative to audience and circumstance. A clock reading alone cannot determine that rhetorical timing is appropriate.