Newsworthiness Proximity Prominence Scoring Grid

Score actual caller annotated newsworthiness criterion grid with explicit weights/maxima and missing-evidence flags, distinguishing available-evidence normalization from full completeness; no objective newsworthiness ranking or fabricated proximity/prominence scores.

Description

Newsworthiness grids depend on explicit scores, weights and evidence.

Newsworthiness grids depend on explicit scores, weights and evidence. Available-evidence normalization must not imply full completeness.^[1]

Enter aligned nonempty score/evidence matrices, nonnegative weights with positive sum and positive maxima. Known scores must be0–maximum; missing scores must be0. Return scores, available-weight fractions, normalized values or −1 and completeness flags.

When to use Newsworthiness Proximity Prominence Scoring Grid

Use Newsworthiness Proximity Prominence Scoring Grid when you need to evaluate the supplied records under the calculation and evidence rules below. Zero observed positive weight can make normalization unavailable. No objective ranking or proximity/prominence scores are invented.

Prepare the actual editorial, source, layout, timing or audience records requested. Supply explicit house-policy limits and preserve missing evidence.

How to use Newsworthiness Proximity Prominence Scoring Grid and understand its results

Prepare the actual editorial, source, layout, timing or audience records requested. Supply explicit house-policy limits and preserve missing evidence.
Enter storyCriterionScores, criterionEvidenceFlags, callerCriterionWeights, callerCriterionMaxima using the field table and calculation rules, then run the tool.
Read availableEvidenceWeightedScores, availableWeightFractions, observedNormalizedScores, completeCriterionEvidenceFlags. The explanation below defines the units, signs, ordering and any flags or placeholder values.
If the tool reports an input issue, correct the values and run it again. Apply the stated assumptions when interpreting a successfully returned result.

Input field	Meaning	Value type; see rules below	Requirement
storyCriterionScores	Story criterion scores	list of list of number	Required
criterionEvidenceFlags	Criterion evidence flags	list of list of number	Required
callerCriterionWeights	Caller criterion weights	list of number	Required
callerCriterionMaxima	Caller criterion maxima	list of number	Required

weightedScore=sum(score×known×weight); normalized=score/sum(known×weight×maximum)

Units, indexing, boundary conditions and exceptional cases are defined in the calculation rules above.

Output field	Meaning	Value type
availableEvidenceWeightedScores	Available evidence weighted scores	list of number
availableWeightFractions	Available weight fractions	list of number
observedNormalizedScores	Observed normalized scores	list of number
completeCriterionEvidenceFlags	Complete criterion evidence flags	list of number

json

{
  "input": {
    "storyCriterionScores": [
      [
        3,
        4
      ],
      [
        5,
        0
      ]
    ],
    "criterionEvidenceFlags": [
      [
        1,
        1
      ],
      [
        1,
        0
      ]
    ],
    "callerCriterionWeights": [
      1,
      2
    ],
    "callerCriterionMaxima": [
      5,
      5
    ]
  },
  "output": {
    "availableEvidenceWeightedScores": [
      11,
      5
    ],
    "availableWeightFractions": [
      1,
      0.3333333333333333
    ],
    "observedNormalizedScores": [
      0.7333333333333333,
      1
    ],
    "completeCriterionEvidenceFlags": [
      1,
      0
    ]
  }
}

Limitations and assumptions

Zero observed positive weight can make normalization unavailable. No objective ranking or proximity/prominence scores are invented.

Numeric inputs, where present, must be finite. Follow the calculation-specific rules for overflow, underflow and exceptional cases. Matrices are limited to 128 rows and 128 entries per row. Numeric lists are limited to 10,000 entries; string lists to 128 strings of at most 128 characters each. The rules above may impose smaller limits. Missing required fields, extra fields, wrong value types and violations of the calculation rules are input errors.

Alternative or Complementary methods

Use Rule of Three Sources Stat Canvass Lookup: A claim-by-source matrix makes corroboration coverage visible. Multiple sources in one dependency group count once for each claim. For a complementary task, use Source Diversity Gender Outlet Percent Tracker: Diversity tallies depend on explicitly reported categories. Shares among known evidence differ from shares among all sources.

References

Newsworthiness Proximity Prominence Scoring Grid — tool.io

Similar or alternative tools

Rule of Three Sources Stat Canvass Lookup
A claim-by-source matrix makes corroboration coverage visible. Multiple sources in one dependency group count once for each claim.
Source Diversity Gender Outlet Percent Tracker
Diversity tallies depend on explicitly reported categories. Shares among known evidence differ from shares among all sources.