Frame Alignment Resonance Score Calculator
Caller calibrated frame-alignment rubric, not guaranteed persuasion. This restricted caller-defined rubric has three independently declared criterion domains: independently assessed frame credibility, independently assessed narrative salience, independently assessed audience resonance. Scores 0–100, nonnegative calibrated weights; known-only normalized mean and unresolved weight retained. No universal validated model, population risk prediction, personal diagnosis or authorization.
Description
Frame resonance requires declared evidence about a movement's framing and audience context.
Frame resonance requires declared evidence about a movement's framing and audience context.1
A calibrated three-domain composite is different from inferring persuasion from text alone.
When to use Frame Alignment Resonance Score Calculator
Use this tool to obtain knownWeightedScore, unknownWeightFraction, unknownCriterionIndices from the supplied records using the stated calculation. Establish the population, period, variable definitions, sample coverage and independently applicable instrument or calibration.
How to use Frame Alignment Resonance Score Calculator and understand its results
- Establish the population, period, variable definitions, sample coverage and independently applicable instrument or calibration.
- Enter independentlyAnnotatedCriterionScores, callerCalibratedCriterionWeights, criterionKnownFlags, using the field meanings and rules below, then run the tool.
- Read knownWeightedScore with unknownWeightFraction and unknownCriterionIndices. A score over known domains does not fill missing evidence.
- Correct any reported input issue and run again. Before using a successful result, check the stated units, signs, reference scope and observation coverage.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| independentlyAnnotatedCriterionScores | Independently annotated criterion scores | list of number | Required |
| callerCalibratedCriterionWeights | Caller calibrated criterion weights | list of number | Required |
| criterionKnownFlags | Criterion known flags; binary 1 means the matched observation is known, 0 means unknown | list of number | Required |
Supply exactly 3 aligned independently calibrated domain scores, nonnegative weights and binary known flags. Every score must lie in [0,100], including unknown placeholders; these are calibrated domain scores rather than raw questionnaire choices. Both total weight and weight attached to known domains must be positive. Average known scores using known weights only; separately return the unknown share of total weight and zero-based unknown indices. Unknown domains are not imputed.
knownWeightedScore=Σ(score_i×weight_i×known_i)/Σ(weight_i×known_i); unknownWeightFraction=Σ(weight_i×(1−known_i))/Σweight_i Use the matching field units and the exact boundary, ordering and reference rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Calculated observation or qualified reference | object |
| result.knownWeightedScore | Weighted mean over known domains only, normalized by their known weight. | number |
| result.unknownWeightFraction | Weight attached to unknown domains divided by total declared weight; fraction, not percent. | number |
| result.unknownCriterionIndices | Zero-based domains whose supplied evidence is marked unknown. | list |
{
"input": {
"independentlyAnnotatedCriterionScores": [
20,
40,
60
],
"callerCalibratedCriterionWeights": [
1,
2,
1
],
"criterionKnownFlags": [
1,
1,
1
]
},
"output": {
"result": {
"knownWeightedScore": 40,
"unknownWeightFraction": 0,
"unknownCriterionIndices": []
}
}
}Limitations and assumptions
Population definitions, survey design, calibration and observation coverage are independently established. A result describes the supplied record or conditional model without demonstrating causation, validating an instrument or supplying representative population evidence.
Missing required fields, unknown fields, wrong types and violated rules return input issues. Numeric values and outputs must be finite. Lists and matrices must be dense, without missing cells. Numeric lists allow at most 10000 entries, subject to the narrower lengths and code rules above. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related step, use Seeman Alienation Score Calculator: Alienation dimensions require an adopted definition and independently coded evidence. For a complementary comparison, use Srole Five-Item Anomie Scale Score: The stated Srole instrument totals five independently coded binary items.
References
Similar or alternative tools
- Seeman Alienation Score Calculator
Alienation dimensions require an adopted definition and independently coded evidence.
- Srole Five-Item Anomie Scale Score
The stated Srole instrument totals five independently coded binary items.