Libel Risk Prepublication Review Checklist

Caller independently documented assertion, identification, verification and applicable legal-review facts, not a liability verdict. 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

Prepublication libel review concerns assertions, identification, verification and applicable legal standards.

Prepublication libel review concerns assertions, identification, verification and applicable legal standards.^[1]

Declared evidence requirements help organize review without deciding liability. The tool compares your observed and known flags with your declared positive requirements, retaining unresolved candidates.

When to use Libel Risk Prepublication Review Checklist

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 Libel Risk Prepublication Review Checklist and understand its results

Establish the reviewed evidence, house style or applicable jurisdiction and dated reference.
Enter callerLibelReviewClassIds, callerLibelReviewRequirements, observedLibelReviewEvidence, knownLibelReviewFlags, 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
callerLibelReviewClassIds	Caller libel review class ids	list of string	Required
callerLibelReviewRequirements	Caller libel review requirements	numeric matrix	Required
observedLibelReviewEvidence	Observed libel review evidence	list of number	Required
knownLibelReviewFlags	Known libel review 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": {
    "callerLibelReviewClassIds": [
      "a",
      "b"
    ],
    "callerLibelReviewRequirements": [
      [
        1,
        0
      ],
      [
        0,
        1
      ]
    ],
    "observedLibelReviewEvidence": [
      1,
      0
    ],
    "knownLibelReviewFlags": [
      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 Actual Malice Public Figure Standard Lookup: Actual-malice terminology concerns a particular legal fault standard rather than ordinary hostility. For a complementary comparison, use Private Figure Negligence Standard Lookup: Fault standards involving a private figure depend on applicable defamation law and the claim's context.

References

Libel Risk Prepublication Review Checklist — tool.io

Similar or alternative tools

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