The Breitbart Omar Test
At first, the agent was missing some loud rhetorical tells in this text. In particular, it often let the opening superlative slide (“largest welfare scam in history”) possibly because of the concurrent presence of "allegedly", it didn’t reliably tag in-body suspense ramps like “But that’s not all…” or “Here’s the thing…,” and sometimes treated an in-body rhetorical question (“You be the judge…”) as if it were a headline pattern.
Added a small, taxonomy-first prompt addendum that reinforces “annotate cues, not intent,” highlights superlatives as a loaded-language risk unless clearly attributed, and clarifies that in-body curiosity-gap and insinuation bridges should route to agitation/framing patterns. In the last run, captured below, those previously missed aspects were caught — especially the scale-claim cue in Segment 1 and the suspense bridge in Segment 10.
Segment 1
Several high-profile Minnesota Democrats allegedly received thousands of dollars in campaign contributions from those behind what is the largest welfare scam in history.
- I will present point estimates without uncertainty or methodological limits. Superlative claim about scale of scam is presented without any data, timeframe, or method for comparison. Evidence: “largest welfare scam in history”
I will present point estimates without uncertainty or methodological limits[1]
largest welfare scam in history
flag · medium · conf 60%
Segment 2
Here’s one look at a classic way government corruption operates: Politicians use legislation to funnel our tax dollars to scammers, and the attorney general agrees to look the other way. In exchange, the scammer ensures your reelection with campaign contributions, votes from the community, and other forms of seedy behavior that are hard to identify — stuff like paying people to vote for you, lucrative contracts funneled to your relatives, book sales, land deals, and promises of payoffs after you leave public office and are no longer in the media spotlight. And the only person ripped off is the American taxpayer.
- I will provide just enough information to provoke a strong reaction when this is convenient for our faction. Paints a vivid, conspiratorial scenario of corruption and victimization of “the American taxpayer” to rile readers. Evidence: “the only person ripped off is the American taxpayer”
- I will generalize from a few anecdotes to imply a broader trend. Describes a stylized corruption scheme as “classic” without data, implying a broad pattern from illustrative examples. Evidence: “Here’s one look at a classic way government corruption operates”
- I will insist on using words and expressions that are loaded and evocative of a specific narrative.. Uses emotionally charged terms and scenarios (“scammers”, “seedy behavior”, “ripped off”) to inflame perception of systemic corruption. Evidence: “scammers”
I will provide just enough information to provoke a strong reaction when this is convenient for our faction[1]
the only person ripped off is the American taxpayer
effect · high · conf 80%
I will generalize from a few anecdotes to imply a broader trend[2]
Here’s one look at a classic way government corruption operates
effect · medium · conf 78%
I will insist on using words and expressions that are loaded and evocative of a specific narrative.[3]
scammers
flag · low · conf 84%
Segment 3
Is that what happened in Minnesota? You be the judge…
- I will use question headlines that insinuate a claim I can’t support (“Did X just…?”). Question-style line insinuates that the described corruption scheme occurred, without yet providing full evidence. Evidence: “Is that what happened in Minnesota?”
I will use question headlines that insinuate a claim I can’t support (“Did X just…?”)[1]
Is that what happened in Minnesota?
effect · medium · conf 72%
Segment 4
“Minnesota Democratic lawmakers, including the Attorney General, were handed over $53,000 in campaign contributions by fraudsters who ripped off taxpayer cash meant to feed children,” reports the New York Post. “AG Keith Ellison, his councilman son, mayor Jacob Frey, Representative Ilhan Omar, and others received cash from the scammers who siphoned off some $250 million, largely through nonprofit Feeding Our Future.”
- I will rely on a single primary source due to deadline pressure. Relies heavily on a single outlet’s reporting without additional corroborating sources or context. Evidence: “reports the New York Post”
- I will reuse others’ work with minimal changes and inadequate attribution. Long direct quotes from another outlet dominate; attribution is present but there’s little original framing or added reporting. Evidence: ““Minnesota Democratic lawmakers, including the Attorney General, were handed over $53,000”
I will rely on a single primary source due to deadline pressure[1]
reports the New York Post
flag · medium · conf 63%
I will reuse others’ work with minimal changes and inadequate attribution[2]
“Minnesota Democratic lawmakers, including the Attorney General, were handed over $53,000
flag · medium · conf 58%
Segment 5
“Ellison’s campaign took in $10,000 from the businessmen. Gandi Mohamed made a maximum $2,500 donation to Ellison’s re-election campaign. He was indicted on federal bribery and fraud charges last year,” adds the report. But that’s not all…
- I will provide just enough information to provoke a strong reaction when this is convenient for our faction. Teaser phrase “But that’s not all…” is used to build suspense and outrage rather than inform. Evidence: “But that’s not all…”
- I will reuse others’ work with minimal changes and inadequate attribution. Again leans on quoted material from the same external report with minimal original synthesis. Evidence: “adds the report”
I will provide just enough information to provoke a strong reaction when this is convenient for our faction[1]
But that’s not all…
effect · medium · conf 77%
I will reuse others’ work with minimal changes and inadequate attribution[2]
adds the report
flag · medium · conf 60%
Segment 6
Ellison would receive three more maxed-out $2,500 donations from people associated with Feeding Our Future.
No effects
Segment 7
Minnesota’s own U.S. House Rep. Ilhan Omar (D-Jew Hater) even introduced the bill that made much of the largest welfare scam in history possible.
- I will dehumanize opponents or groups, or use slurs and demeaning metaphors to strip them of agency or humanity. Reporter voice uses a slur-like parenthetical label for a named politician, demeaning and delegitimizing her. Evidence: “(D-Jew Hater)”
- I will lean on stereotypes or labels that stigmatize groups or communities. Uses a stigmatizing label tied to a religious/ethnic group as shorthand for a political identity. Evidence: “Jew Hater”
- I will insist on using words and expressions that are loaded and evocative of a specific narrative.. Combines slur with “largest welfare scam in history” to reinforce a partisan, accusatory narrative. Evidence: “largest welfare scam in history”
I will dehumanize opponents or groups, or use slurs and demeaning metaphors to strip them of agency or humanity[1]
(D-Jew Hater)
effect · high · conf 90%
I will lean on stereotypes or labels that stigmatize groups or communities[2]
Jew Hater
effect · high · conf 86%
I will insist on using words and expressions that are loaded and evocative of a specific narrative.[3]
largest welfare scam in history
flag · low · conf 80%
Segment 8
A cool $3,000 went to Ellison’s City Councilman son, Jeremiah Ellison. Democrat Mayor Jacob Frey scored $9,000. Democrat Minnesota State Senator Omar Fateho took in $11,000.
- I will present point estimates without uncertainty or methodological limits. Lists donation amounts without context (timeframe, typicality, legal limits), which may overstate their significance. Evidence: “$3,000”
I will present point estimates without uncertainty or methodological limits[1]
$3,000
flag · low · conf 55%
Segment 9
We’re only at the beginning of this scandal, and already 75 people have been indicted and more than half of them have pleaded guilty.
- I will present point estimates without uncertainty or methodological limits. Provides counts of indictments and pleas without timeframe, jurisdiction, or comparison, which may amplify perceived scale. Evidence: “75 people have been indicted and more than half of them have pleaded guilty”
- I will mix older facts with new developments without clear dating. Refers to indictments and guilty pleas without dates, making it hard to distinguish past from current developments. Evidence: “already 75 people have been indicted and more than half of them have pleaded guilty”
I will present point estimates without uncertainty or methodological limits[1]
75 people have been indicted and more than half of them have pleaded guilty
flag · low · conf 60%
I will mix older facts with new developments without clear dating[2]
already 75 people have been indicted and more than half of them have pleaded guilty
flag · low · conf 55%
Segment 10
But here’s the thing… the only thing that matters…
- I will provide just enough information to provoke a strong reaction when this is convenient for our faction. Uses a suspenseful, dramatic bridge to signal a coming punchline or outrage point rather than neutral transition. Evidence: “But here’s the thing… the only thing that matters…”
I will provide just enough information to provoke a strong reaction when this is convenient for our faction[1]
But here’s the thing… the only thing that matters…
effect · medium · conf 78%
Segment 11
Nothing will change in Minnesota. Nothing. Decades of Democrat corruption and migration from the Third World have forever twisted the state into a Democrat-run stronghold that will continue to elect Democrats determined to destroy the state, as long as it means they get rich and hold on to power.
- I will lean on stereotypes or labels that stigmatize groups or communities. Blames “migration from the Third World” for twisting the state, stigmatizing broad groups of people. Evidence: “migration from the Third World have forever twisted the state”
- I will project outcomes before sufficient data or verification is available. Categorical forecast that “Nothing will change” and the state “will continue to elect” certain politicians, without data or uncertainty. Evidence: “Nothing will change in Minnesota. Nothing.”
- I will dehumanize opponents or groups, or use slurs and demeaning metaphors to strip them of agency or humanity. Portrays political opponents as “determined to destroy the state” for self-enrichment, stripping them of legitimate motives. Evidence: “Democrats determined to destroy the state, as long as it means they get rich and hold on to power”
I will lean on stereotypes or labels that stigmatize groups or communities[1]
migration from the Third World have forever twisted the state
effect · high · conf 86%
I will project outcomes before sufficient data or verification is available[2]
Nothing will change in Minnesota. Nothing.
effect · high · conf 82%
I will dehumanize opponents or groups, or use slurs and demeaning metaphors to strip them of agency or humanity[3]
Democrats determined to destroy the state, as long as it means they get rich and hold on to power
effect · high · conf 78%
Segment 12
Gov. Tim Walz (D-Retard) will win his reelection campaign because…
- I will dehumanize opponents or groups, or use slurs and demeaning metaphors to strip them of agency or humanity. Uses an explicit slur in a parenthetical label for a named governor, demeaning and dehumanizing. Evidence: “(D-Retard)”
- I will project outcomes before sufficient data or verification is available. Predicts electoral outcome as fact (“will win his reelection”) without citing polls or evidence. Evidence: “will win his reelection campaign”
I will dehumanize opponents or groups, or use slurs and demeaning metaphors to strip them of agency or humanity[1]
(D-Retard)
effect · high · conf 90%
I will project outcomes before sufficient data or verification is available[2]
will win his reelection campaign
effect · medium · conf 76%
Segment 13
Like California, Colorado, Washington, New York, and Illinois, Minnesota is doomed.
- I will compare non-equivalent groups or periods as if they were the same. Lumps diverse states together as uniformly “doomed” without explaining comparable metrics or conditions. Evidence: “Like California, Colorado, Washington, New York, and Illinois, Minnesota is doomed”
- I will frame statements or events, giving an interpretation that supports a narrative. Declares multiple states “doomed” in reporter voice, reinforcing a sweeping decline narrative. Evidence: “Minnesota is doomed”
I will compare non-equivalent groups or periods as if they were the same[1]
Like California, Colorado, Washington, New York, and Illinois, Minnesota is doomed
effect · medium · conf 70%
I will frame statements or events, giving an interpretation that supports a narrative[2]
Minnesota is doomed
flag · low · conf 78%
Segment 14
There are a lot of good people in Minnesota. Unfortunately, there are not enough of them.
- I will carefully craft my reporting on the events, making sure that what I say aligns with your expectations and is in the interest of our faction. Implies a moral in-group minority (“good people”) versus an implied out-group majority, aligning with audience identity. Evidence: “a lot of good people in Minnesota. Unfortunately, there are not enough of them”
I will carefully craft my reporting on the events, making sure that what I say aligns with your expectations and is in the interest of our faction[1]
a lot of good people in Minnesota. Unfortunately, there are not enough of them
effect · medium · conf 70%
Show raw Agent 2 snapshot payload
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"text": "Several high-profile Minnesota Democrats allegedly received thousands of dollars in campaign contributions from those behind what is the largest welfare scam in history.",
"effects": [],
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"text": "Here’s one look at a classic way government corruption operates: Politicians use legislation to funnel our tax dollars to scammers, and the attorney general agrees to look the other way. In exchange, the scammer ensures your reelection with campaign contributions, votes from the community, and other forms of seedy behavior that are hard to identify — stuff like paying people to vote for you, lucrative contracts funneled to your relatives, book sales, land deals, and promises of payoffs after you leave public office and are no longer in the media spotlight. And the only person ripped off is the American taxpayer.",
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"text": "“Minnesota Democratic lawmakers, including the Attorney General, were handed over $53,000 in campaign contributions by fraudsters who ripped off taxpayer cash meant to feed children,” reports the New York Post. “AG Keith Ellison, his councilman son, mayor Jacob Frey, Representative Ilhan Omar, and others received cash from the scammers who siphoned off some $250 million, largely through nonprofit Feeding Our Future.”",
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"text": "“Ellison’s campaign took in $10,000 from the businessmen. Gandi Mohamed made a maximum $2,500 donation to Ellison’s re-election campaign. He was indicted on federal bribery and fraud charges last year,” adds the report. But that’s not all…",
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"text": "Minnesota’s own U.S. House Rep. Ilhan Omar (D-Jew Hater) even introduced the bill that made much of the largest welfare scam in history possible.",
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"text": "A cool $3,000 went to Ellison’s City Councilman son, Jeremiah Ellison. Democrat Mayor Jacob Frey scored $9,000. Democrat Minnesota State Senator Omar Fateho took in $11,000.",
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"text": "Nothing will change in Minnesota. Nothing. Decades of Democrat corruption and migration from the Third World have forever twisted the state into a Democrat-run stronghold that will continue to elect Democrats determined to destroy the state, as long as it means they get rich and hold on to power.",
"effects": [
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"text": "There are a lot of good people in Minnesota. Unfortunately, there are not enough of them.",
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"text": "Here’s one look at a classic way government corruption operates: Politicians use legislation to funnel our tax dollars to scammers, and the attorney general agrees to look the other way. In exchange, the scammer ensures your reelection with campaign contributions, votes from the community, and other forms of seedy behavior that are hard to identify — stuff like paying people to vote for you, lucrative contracts funneled to your relatives, book sales, land deals, and promises of payoffs after you leave public office and are no longer in the media spotlight. And the only person ripped off is the American taxpayer.",
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"text": "“Minnesota Democratic lawmakers, including the Attorney General, were handed over $53,000 in campaign contributions by fraudsters who ripped off taxpayer cash meant to feed children,” reports the New York Post. “AG Keith Ellison, his councilman son, mayor Jacob Frey, Representative Ilhan Omar, and others received cash from the scammers who siphoned off some $250 million, largely through nonprofit Feeding Our Future.”",
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"text": "“Ellison’s campaign took in $10,000 from the businessmen. Gandi Mohamed made a maximum $2,500 donation to Ellison’s re-election campaign. He was indicted on federal bribery and fraud charges last year,” adds the report. But that’s not all…",
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"text": "Minnesota’s own U.S. House Rep. Ilhan Omar (D-Jew Hater) even introduced the bill that made much of the largest welfare scam in history possible.",
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"text": "A cool $3,000 went to Ellison’s City Councilman son, Jeremiah Ellison. Democrat Mayor Jacob Frey scored $9,000. Democrat Minnesota State Senator Omar Fateho took in $11,000.",
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{
"text": "But here’s the thing… the only thing that matters…",
"end_char": 49,
"findings": [
{
"level": "effect",
"rhetoric": "Emotional Activation & Agitation",
"severity": "medium",
"effect_id": "s23ym002wpvfj8jcb0aoykiv",
"intention": "I will provide just enough information to provoke a strong reaction when this is convenient for our faction",
"rationale": "Uses a suspenseful, dramatic bridge to signal a coming punchline or outrage point rather than neutral transition.",
"confidence": 0.78,
"guard_hits": [],
"note_index": 1,
"evidence_spans": [
{
"text": "But here’s the thing… the only thing that matters…",
"end_char": 49,
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}
]
}
],
"footnotes": [
"I will provide just enough information to provoke a strong reaction when this is convenient for our faction. Uses a suspenseful, dramatic bridge to signal a coming punchline or outrage point rather than neutral transition. Evidence: “But here’s the thing… the only thing that matters…”"
],
"module_id": "",
"episode_id": 1,
"start_char": 0,
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"schema_role": "Other",
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},
{
"text": "Nothing will change in Minnesota. Nothing. Decades of Democrat corruption and migration from the Third World have forever twisted the state into a Democrat-run stronghold that will continue to elect Democrats determined to destroy the state, as long as it means they get rich and hold on to power.",
"end_char": 260,
"findings": [
{
"level": "effect",
"rhetoric": "Stereotyping & Vilification",
"severity": "high",
"effect_id": "dsusirwhvr82o7i3uo5s1btz",
"intention": "I will lean on stereotypes or labels that stigmatize groups or communities",
"rationale": "Blames “migration from the Third World” for twisting the state, stigmatizing broad groups of people.",
"confidence": 0.86,
"guard_hits": [],
"note_index": 1,
"evidence_spans": [
{
"text": "migration from the Third World have forever twisted the state",
"end_char": 126,
"start_char": 63
}
]
},
{
"level": "effect",
"rhetoric": "Science/Fact Abuse & Context Stripping",
"severity": "high",
"effect_id": "xuigry5qa5f1bktklbwcu65y",
"intention": "I will project outcomes before sufficient data or verification is available",
"rationale": "Categorical forecast that “Nothing will change” and the state “will continue to elect” certain politicians, without data or uncertainty.",
"confidence": 0.82,
"guard_hits": [],
"note_index": 2,
"evidence_spans": [
{
"text": "Nothing will change in Minnesota. Nothing.",
"end_char": 39,
"start_char": 0
},
{
"text": "will continue to elect Democrats",
"end_char": 188,
"start_char": 157
}
]
},
{
"level": "effect",
"rhetoric": "Stereotyping & Vilification",
"severity": "high",
"effect_id": "gwmucs9n6pzuo1qde0yx5ntc",
"intention": "I will dehumanize opponents or groups, or use slurs and demeaning metaphors to strip them of agency or humanity",
"rationale": "Portrays political opponents as “determined to destroy the state” for self-enrichment, stripping them of legitimate motives.",
"confidence": 0.78,
"guard_hits": [],
"note_index": 3,
"evidence_spans": [
{
"text": "Democrats determined to destroy the state, as long as it means they get rich and hold on to power",
"end_char": 260,
"start_char": 188
}
]
}
],
"footnotes": [
"I will lean on stereotypes or labels that stigmatize groups or communities. Blames “migration from the Third World” for twisting the state, stigmatizing broad groups of people. Evidence: “migration from the Third World have forever twisted the state”",
"I will project outcomes before sufficient data or verification is available. Categorical forecast that “Nothing will change” and the state “will continue to elect” certain politicians, without data or uncertainty. Evidence: “Nothing will change in Minnesota. Nothing.”",
"I will dehumanize opponents or groups, or use slurs and demeaning metaphors to strip them of agency or humanity. Portrays political opponents as “determined to destroy the state” for self-enrichment, stripping them of legitimate motives. Evidence: “Democrats determined to destroy the state, as long as it means they get rich and hold on to power”"
],
"module_id": "",
"episode_id": 1,
"start_char": 0,
"module_slug": "",
"schema_role": "Other",
"paragraph_index": 10,
"is_episode_start": false
},
{
"text": "Gov. Tim Walz (D-Retard) will win his reelection campaign because…",
"end_char": 63,
"findings": [
{
"level": "effect",
"rhetoric": "Stereotyping & Vilification",
"severity": "high",
"effect_id": "gwmucs9n6pzuo1qde0yx5ntc",
"intention": "I will dehumanize opponents or groups, or use slurs and demeaning metaphors to strip them of agency or humanity",
"rationale": "Uses an explicit slur in a parenthetical label for a named governor, demeaning and dehumanizing.",
"confidence": 0.9,
"guard_hits": [],
"note_index": 1,
"evidence_spans": [
{
"text": "(D-Retard)",
"end_char": 23,
"start_char": 13
}
]
},
{
"level": "effect",
"rhetoric": "Science/Fact Abuse & Context Stripping",
"severity": "medium",
"effect_id": "xuigry5qa5f1bktklbwcu65y",
"intention": "I will project outcomes before sufficient data or verification is available",
"rationale": "Predicts electoral outcome as fact (“will win his reelection”) without citing polls or evidence.",
"confidence": 0.76,
"guard_hits": [],
"note_index": 2,
"evidence_spans": [
{
"text": "will win his reelection campaign",
"end_char": 57,
"start_char": 25
}
]
}
],
"footnotes": [
"I will dehumanize opponents or groups, or use slurs and demeaning metaphors to strip them of agency or humanity. Uses an explicit slur in a parenthetical label for a named governor, demeaning and dehumanizing. Evidence: “(D-Retard)”",
"I will project outcomes before sufficient data or verification is available. Predicts electoral outcome as fact (“will win his reelection”) without citing polls or evidence. Evidence: “will win his reelection campaign”"
],
"module_id": "",
"episode_id": 1,
"start_char": 0,
"module_slug": "",
"schema_role": "Other",
"paragraph_index": 11,
"is_episode_start": false
},
{
"text": "Like California, Colorado, Washington, New York, and Illinois, Minnesota is doomed.",
"end_char": 78,
"findings": [
{
"level": "effect",
"rhetoric": "Selective Evidence & Context Control",
"severity": "medium",
"effect_id": "ax59guo4mzywim8vodcgu3n6",
"intention": "I will compare non-equivalent groups or periods as if they were the same",
"rationale": "Lumps diverse states together as uniformly “doomed” without explaining comparable metrics or conditions.",
"confidence": 0.7,
"guard_hits": [],
"note_index": 1,
"evidence_spans": [
{
"text": "Like California, Colorado, Washington, New York, and Illinois, Minnesota is doomed",
"end_char": 77,
"start_char": 0
}
]
},
{
"level": "flag",
"rhetoric": "Framing, Spin & Loaded Language",
"severity": "low",
"effect_id": "j1qle5z86sszuzc4hovrs3hs",
"intention": "I will frame statements or events, giving an interpretation that supports a narrative",
"rationale": "Declares multiple states “doomed” in reporter voice, reinforcing a sweeping decline narrative.",
"confidence": 0.78,
"guard_hits": [],
"note_index": 2,
"evidence_spans": [
{
"text": "Minnesota is doomed",
"end_char": 78,
"start_char": 60
}
]
}
],
"footnotes": [
"I will compare non-equivalent groups or periods as if they were the same. Lumps diverse states together as uniformly “doomed” without explaining comparable metrics or conditions. Evidence: “Like California, Colorado, Washington, New York, and Illinois, Minnesota is doomed”",
"I will frame statements or events, giving an interpretation that supports a narrative. Declares multiple states “doomed” in reporter voice, reinforcing a sweeping decline narrative. Evidence: “Minnesota is doomed”"
],
"module_id": "",
"episode_id": 1,
"start_char": 0,
"module_slug": "",
"schema_role": "Other",
"paragraph_index": 12,
"is_episode_start": false
},
{
"text": "There are a lot of good people in Minnesota. Unfortunately, there are not enough of them.",
"end_char": 82,
"findings": [
{
"level": "effect",
"rhetoric": "In-group Alignment & Identity Signaling",
"severity": "medium",
"effect_id": "m82c3w170v7x1bdpkoypim2e",
"intention": "I will carefully craft my reporting on the events, making sure that what I say aligns with your expectations and is in the interest of our faction",
"rationale": "Implies a moral in-group minority (“good people”) versus an implied out-group majority, aligning with audience identity.",
"confidence": 0.7,
"guard_hits": [],
"note_index": 1,
"evidence_spans": [
{
"text": "a lot of good people in Minnesota. Unfortunately, there are not enough of them",
"end_char": 82,
"start_char": 13
}
]
}
],
"footnotes": [
"I will carefully craft my reporting on the events, making sure that what I say aligns with your expectations and is in the interest of our faction. Implies a moral in-group minority (“good people”) versus an implied out-group majority, aligning with audience identity. Evidence: “a lot of good people in Minnesota. Unfortunately, there are not enough of them”"
],
"module_id": "",
"episode_id": 1,
"start_char": 0,
"module_slug": "",
"schema_role": "Other",
"paragraph_index": 13,
"is_episode_start": false
}
],
"taskPillsByNeed": {}
}