Opinion Privilege Substantial Accuracy Checker

Caller independently verified factual implication/context/accuracy and applicable privilege review evidence; opinion label alone not defamation immunity. Independently declared positive evidence requirements; overlaps and unknowns retained. Caller independently verifies context, evidence, reference edition and applicability; not factual authenticity, current legal advice or publication clearance.

Description

An opinion label does not by itself settle questions about implied facts or applicable privilege.

An opinion label does not by itself settle questions about implied facts or applicable privilege.^[1]

Review needs independently established context and the declared factual-accuracy criteria. The tool compares your observed and known flags with your declared positive requirements, retaining unresolved candidates.

When to use Opinion Privilege Substantial Accuracy Checker

Use this tool to calculate or review the supplied observations under the stated rules. Establish the reviewed evidence, house style or applicable jurisdiction and dated reference.

How to use Opinion Privilege Substantial Accuracy Checker and understand its results

Establish the reviewed evidence, house style or applicable jurisdiction and dated reference.
Enter callerOpinionPrivilegeClassIds, callerOpinionPrivilegeRequirements, observedOpinionPrivilegeEvidence, knownOpinionPrivilegeFlags, using the field meanings and rules below, then run the tool.
Read compatibleCallerClasses, fullyWitnessedCallerClasses, using the output meanings and calculation rules below. Distinguish compatible candidates from fully witnessed classes; compatibility can retain unknown required 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
callerOpinionPrivilegeClassIds	Caller opinion privilege class ids	list of string	Required
callerOpinionPrivilegeRequirements	Caller opinion privilege requirements	numeric matrix	Required
observedOpinionPrivilegeEvidence	Observed opinion privilege evidence	list of number	Required
knownOpinionPrivilegeFlags	Known opinion privilege flags; binary 1 means the matched observation is known, 0 means unknown	list of number	Required

Use unique nonblank class IDs and a nonempty binary requirement matrix aligned by class and evidence column. Every class must require at least one positive feature. A one requires presence; zero ignores the feature and does not require absence. Supply binary observed evidence and known flags. A known required zero excludes a class; an unknown required feature retains compatibility but prevents full witnessing. Return all compatible classes and the subset with every required feature known and present. Classes may overlap.

compatible_c=∀j(requirement_cj=0 or known_j=0 or evidence_j=1); witnessed_c=∀j(requirement_cj=0 or (known_j=1 and evidence_j=1))

Use the matching field units and the exact boundary, ordering and reference rules above.

Output field	Meaning	Value type
compatibleCallerClasses	Compatible caller classes	list of string
fullyWitnessedCallerClasses	Fully witnessed caller classes	list of string

json

{
  "input": {
    "callerOpinionPrivilegeClassIds": [
      "a",
      "b"
    ],
    "callerOpinionPrivilegeRequirements": [
      [
        1,
        0
      ],
      [
        0,
        1
      ]
    ],
    "observedOpinionPrivilegeEvidence": [
      1,
      0
    ],
    "knownOpinionPrivilegeFlags": [
      1,
      0
    ]
  },
  "output": {
    "compatibleCallerClasses": [
      "a",
      "b"
    ],
    "fullyWitnessedCallerClasses": [
      "a"
    ]
  }
}

Limitations and assumptions

Annotations and applicable policies must be independently reviewed. The tool does not authenticate facts, establish current law or grant publication clearance. Compatible classes with unknown evidence are not positive identifications. Fully witnessed classes satisfy only the declared positive requirements.

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; ordinary string lists allow at most 128 strings of 128 UTF-16 units each; numeric matrices allow at most 128 rows and 128 columns, subject to narrower requirements above.

Alternative or Complementary methods

For a related step, use Private Figure Negligence Standard Lookup: Fault standards involving a private figure depend on applicable defamation law and the claim's context. For a complementary comparison, use Actual Malice Public Figure Standard Lookup: Actual-malice terminology concerns a particular legal fault standard rather than ordinary hostility.

References

Opinion Privilege Substantial Accuracy Checker — tool.io

Similar or alternative tools

Private Figure Negligence Standard Lookup
Fault standards involving a private figure depend on applicable defamation law and the claim's context.
Actual Malice Public Figure Standard Lookup
Actual-malice terminology concerns a particular legal fault standard rather than ordinary hostility.