Deux articles, un même fait. Que disent-ils vraiment ?
https://infoscore.coThis text is a methodological description of the Info Score tool itself, explaining its purpose, methodology, and pedagogical approach for media literacy education. The document outlines how Info Score performs comparative analysis between two articles on the same topic, extracting observable variables to produce a grade from A to E.
Sub-scores
Representative quotes
- « Info Score ne décide pas de la vérité. »
- « Un outil pour la classe, pas un verdict. »
- « Une fonction déterministe applique ensuite les règles publiques (rules_v0.yaml) pour produire une note de A à E. »
Methodological notes
This text is a self-referential description of the Info Score tool rather than an article to be evaluated. It contains no external sources, no factual claims about the external world, and no contested information. The document serves as a methodological preamble explaining the framework. All claims are self-descriptive and therefore not attributable to external sources. No hedging markers are present.
Computation detail
Each step is reproducible from the extracted variables.
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Fact / commentary separation
- attribution_ratio = 0/5 = 0.000
- unmarked_density = 2/140 mots → 14.29/1000 → pénalité 1.000
- marker_bonus (transparence) = 0.150
- score = clamp(0.55·0.000 + 0.45·(1-1.000) + 0.150) = 0.150
Variables used
{
"claims_total": 5,
"claims_with_attribution": 0,
"opinion_markers_count": 2,
"unmarked_opinions_presented_as_facts": 2,
"word_count": 140
}
Source traceability
- named_density = 0/200 mots → score nommé 0.000
- non_anon_ratio = 0/1 = 0.500
- primary_ratio = 0/1 = 0.000
- links_bonus = 0 liens → 0.000
- score = clamp(0.35·0.000 + 0.30·0.500 + 0.25·0.000 + 0.000) = 0.150
Variables used
{
"external_links_count": 0,
"sources_anonymous_count": 0,
"sources_named_count": 0,
"sources_primary_count": 0
}
Plurality of viewpoints
- subject_type = factuel_incontesté (pluralité moins exigible)
- score = clamp(0.5 + 1·0.2) = 0.700
Variables used
{
"distinct_viewpoints_count": 1,
"subject_type": "factuel_incontest\u00e9"
}
Expression of uncertainty
- calibration = bien_calibré → 1.00
- hedging_present = False → bonus 0.00
- premature_conclusions = 0 → pénalité 0.000
- score = clamp(1.00 + 0.00 - 0.000) = 1.000
Variables used
{
"confidence_calibration": "bien_calibr\u00e9",
"hedging_present": false,
"premature_conclusions_count": 0
}
Tone and lexicon
- intensity = neutre → base 1.00
- loaded_density = 5.00/1000 mots → pénalité 0.219
- ad_hominem = 0 → pénalité 0.000
- rhetorical = 0 → pénalité 0.000
- sensational_headline = False → 0.00
- score = clamp(1.00 - 0.219 - 0.000 - 0.000 - 0.00) = 0.781
Variables used
{
"ad_hominem_attacks_count": 0,
"emotional_charge_intensity": "neutre",
"loaded_words_count": 1,
"rhetorical_questions_count": 0,
"sensationalist_headline": false
}
Contextualisation
- completeness = non_nécessaire → base 0.85
- perspectives_bonus (hist=False, géo=False, chiffres_verif=True) = 0.10
- missing_essential_info = 0 → pénalité 0.000
- score = clamp(0.85 + 0.10 - 0.000) = 0.950
Variables used
{
"dates_and_figures_verifiable": true,
"geographic_context_provided": false,
"historical_context_provided": false,
"missing_essential_info_count": 0,
"relevant_background_complete": "non_n\u00e9cessaire"
}