Appraisal Macrovaluation Score Weighted Criteria Calculator
Restricted institution-calibrated archival macroappraisal arithmetic, not automatic retention/disposal authority or universal appraisal model. This restricted caller-defined rubric has three independently declared criterion domains: independently assessed societal/function significance, independently assessed evidential value, independently assessed representational/community value. 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
Macrovaluation summarizes criteria for appraisal at an adopted institutional level.
Macrovaluation summarizes criteria for appraisal at an adopted institutional level.1
A custom weighted score does not itself authorize retention or disposal. Use this tool to summarize known criterion scores under caller weights and retain missing-evidence coverage.
When to use Appraisal Macrovaluation Score Weighted Criteria Calculator
Use this tool to summarize known criterion scores under caller weights and retain missing-evidence coverage. Establish the institution, collection/material context, applicable standards or authority, reference date and measurement units.
How to use Appraisal Macrovaluation Score Weighted Criteria Calculator and understand its results
- Establish the institution, collection/material context, applicable standards or authority, reference date and measurement units.
- 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 does not itself authorize retention or disposal. 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 Permanent Retention Flag by Record Series Lookup to examine a related aspect: Permanent-retention decisions belong to an authorized record-series schedule.
Use Disposal Certificate Batch Reference Generator where its separate inputs and scope apply: A disposal batch reference links approved record IDs to an institution and authorized batch.
References
Similar or alternative tools
- Permanent Retention Flag by Record Series Lookup
Permanent-retention decisions belong to an authorized record-series schedule.
- Disposal Certificate Batch Reference Generator
A disposal batch reference links approved record IDs to an institution and authorized batch.