Revictimization Risk Factor Score Calculator
Restricted caller-calibrated revictimization evidence score, not blame the victim or predict an individual outcome. This restricted caller-defined rubric has three independently declared criterion domains: independently documented prior victimization pattern, independently assessed exposure circumstances, independently assessed protective-context gaps. 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
Revictimization research uses defined outcomes, observations and model assumptions.
Revictimization research uses defined outcomes, observations and model assumptions.1
A custom weighted score summarizes supplied evidence rather than estimating a validated personal risk. Use this tool to summarize known criterion scores under caller weights and retain missing-evidence coverage.
When to use Revictimization Risk Factor Score Calculator
Use this tool to summarize known criterion scores under caller weights and retain missing-evidence coverage. Establish the jurisdiction or research scope, observation and event definitions, reference date, matched period and denominator basis.
How to use Revictimization Risk Factor Score Calculator and understand its results
- Establish the jurisdiction or research scope, observation and event definitions, reference date, matched period and denominator basis.
- Enter independentlyAnnotatedCriterionScores, callerCalibratedCriterionWeights, criterionKnownFlags, using the field meanings and rules below, then run the tool.
- Read result.knownWeightedScore, result.unknownWeightFraction, result.unknownCriterionIndices. Interpret each field using the units, signs and evidence rules below.
- If input validation fails, correct the reported field and run again. Before applying a successful result, check the stated reference scope, units, assumptions and any retained unknown evidence.
| 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; 1 means known and 0 means unknown; keep the same item order | list of number | Required |
Enter exactly three aligned finite criterion scores, nonnegative weights and binary known flags. Scores must lie in 0–100 even when marked unknown. Require positive total weight and positive known weight; known items with only zero weights do not suffice. Divide the known weighted sum by known weight, report unknown weight divided by total weight, and retain zero-based unknown indices. The generic rubric neither supplies an official instrument nor calibrates a probability.
knownScore=Σ(score_i×weight_i×known_i)/Σ(weight_i×known_i); unknownWeightFraction=Σ(weight_i×(1−known_i))/Σweight_i Apply the field units and exact ordering, boundary and evidence rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Structured result containing the calculation or selected reference fields below | object |
| result.knownWeightedScore | Weighted mean over known criteria with positive known weight, on the supplied 0–100 scale | number |
| result.unknownWeightFraction | Unknown criterion weight divided by total criterion weight | number |
| result.unknownCriterionIndices | Zero-based positions of unknown criteria | list of values |
{
"input": {
"independentlyAnnotatedCriterionScores": [
20,
40,
60
],
"callerCalibratedCriterionWeights": [
1,
2,
1
],
"criterionKnownFlags": [
1,
1,
1
]
},
"output": {
"result": {
"knownWeightedScore": 40,
"unknownWeightFraction": 0,
"unknownCriterionIndices": []
}
}
}Limitations and assumptions
A custom weighted score summarizes supplied evidence rather than estimating a validated personal risk. Results depend on the independently established measurement basis and the exact model assumptions above.
Every listed input requires an explicit value. Unexpected fields, incorrect value types, nonfinite numbers and sparse numeric arrays fail validation; tool-specific bounds and alignment rules apply as described above. Extremely large inputs or outputs can also be rejected. An input error is distinct from a successful calculation that reports a false comparison or retains unknown evidence.
Alternative or Complementary methods
Use Repeat Victimization Within Year Flag Checker to examine a related aspect: Repeat victimization requires distinct recorded events linked to the same adopted victim identifier.
Use Victimization Rate Per 1000 Survey Style Calculator where its separate inputs and scope apply: Victimization rates compare surveyed events with a matched exposure denominator.
References
Similar or alternative tools
- Repeat Victimization Within Year Flag Checker
Repeat victimization requires distinct recorded events linked to the same adopted victim identifier.
- Victimization Rate Per 1000 Survey Style Calculator
Victimization rates compare surveyed events with a matched exposure denominator.