Debate Quality
Debate Quality measures reasoning — a debater can lose while arguing well.
Debate ID
TD-6M3K2P
Open DiscussionOpenWeakShould anonymized public-health datasets always be open by default?
Should anonymized public-health datasets always be open by default?
Context
Debaters test transparency gains against re-identification risks.
Debate Completion (v1)
ActiveTheDebator rewards finishing debates, honest concessions, and intellectual accountability.
Reliability
35/100
Completion Rate
0%
Active · Reliability 35/100
Debate Outcome (v1)
ActiveTheDebator is not designed to create champions. It is designed to create stronger thinkers. Victory is secondary to intellectual honesty, reasoning quality, burden fulfillment, question accountability, and steelmanning.
Debate Completion
Debate Completion measures follow-through — separate from who won.
Debate Outcome
Active — outcome pending
- • Debate is active — outcome pending.
TheDebator rewards intellectual rigor more than victory. Winning is an outcome. Reputation is a behavior. The two should never be merged.
AI Judge (preview)
Winner DeterminedHigh confidenceAI-assisted mock judgment.AI Judge preview — final judgment available after resolution.
AI Judge evaluates debate performance, not personal worth.
TheDebator should reward users for staying focused on the actual debate, not for pivoting, dodging, or changing the subject.
Topic adherence summary
@patn_data maintained the tightest focus on “Should anonymized public-health datasets always be open by default?”. Both sides remained reasonably anchored to the central disagreement. Flags: Stayed On Topic.
Debate topic adherence score: 88/100
Stayed On Topic
AI Judge Scorecard
Composite 61/100
Topic Adherence
88/ 100Weight 25%
Argument Quality
88/ 100Weight 18%
Evidence Integrity
51/ 100Weight 18%
Question Accountability
0/ 100Weight 14%
Burden of Proof
50/ 100Weight 14%
Steelmanning
50/ 100Weight 6%
Civility / Good Faith
80/ 100Weight 5%
Weighted: Topic 25% · Argument 18% · Evidence Integrity 18% · Questions 14.000000000000002% · Burden 14.000000000000002% · Steelman 6% · Civility 5%
Evidence Analysis (v2)
Evidence should support the specific claim under debate — classification, relevance, and source credibility are scored separately from argument style.
Debate evidence integrity: 52/100
Evidence Review
0/100No evidence artifacts were classified — claims rely on assertion until sources are submitted.
Source Review
Sources are bucketed by type (primary, academic, news, anecdotal, etc.) to separate provenance from rhetorical quality.
Credibility Review
0/100Average source credibility 0/100 — weighted by publisher tier, citation signals, community votes, and recency.
- • Publisher reputation inferred from classification tier (mock heuristic).
- • Citation quality boosted when labels include references or DOI-style markers.
- • No strong misuse patterns detected in submitted labels.
Evidence Gap Review
Gaps highlight missing premise-linked sources, weak credibility, and unsupported strong claims.
- • No classified evidence submissions linked to this debater.
Per-debater evidence
@patn_data
Integrity 52/100Evidence gaps
- • No classified evidence submissions linked to this debater.
Per-debater topic adherence
@patn_data
Topic 88/100Stayed On Topic
No additional topic notes.
Performance analysis (not a simple win call)
- • @patn_data topic adherence scored 88/100 against the premise.
- • Burden partially met — outstanding support requested.
- • Evidence gaps: No classified evidence submissions linked to this debater.
- • Debate-wide composite 61/100 — topic adherence weighted at 25%.
- • TheDebator should reward users for staying focused on the actual debate, not for pivoting, dodging, or changing the subject.
- • No evidence artifacts were classified — claims rely on assertion until sources are submitted.
- • Average source credibility 0/100 — weighted by publisher tier, citation signals, community votes, and recency.
Topic-weighted performance edge: @patn_data(not declared absolute truth)
AI-assisted mock judgment. @patn_data maintained the tightest focus on “Should anonymized public-health datasets always be open by default?”. Both sides remained reasonably anchored to the central disagreement. Flags: Stayed On Topic. @patn_data topic adherence scored 88/100 against the premise. Performance edge (topic-weighted): @patn_data — High confidence; inspect reasoning before treating as final.
Strongest arguments
- • @patn_data: Open-by-default can work if redaction standards are versioned and independently audited.
Weakest arguments
- • @patn_data: Open-by-default can work if redaction standards are versioned and independently audited.
Evidence gaps
- • No classified evidence submissions linked to this debater.
Burden of proof
Burden partially met — outstanding support requested.
AI Judge may be wrong. Users should inspect the reasoning.
17 Intellectual Outcomes74 supports · 58 counterpoints · 39 evidence
Share Debate
TD-6M3K2P
Open DiscussionStalledActiveShould anonymized public-health datasets always be open by default?
55 participants · Direct URL below
/debate/TD-6M3K2P
Initial Stance
Pat N.
Open-by-default can work if redaction standards are versioned and independently audited.
Main Actions
Respond with structured reasoning — Support, Counterpoint, Question, or Takeaway.
Debate Outcomes
Intellectual outcomes logged across this debate — learning, understanding, common ground, and open questions.
Total Takeaways: 17
Top Takeaways
Learned New Relevant Information: 0Gained Understanding: 10Found Common Ground: 0Changed My Mind: 0Need More Evidence: 7
Analytics Preview (Mock)
Total Takeaways
17
Learning Events
0
Understanding Events
10
Common Ground Events
0
Mind Changes
0
Evidence Requests
7
Question Accountability
Answer rate
0%
Questions asked
0
Avoided
0
Response Composer
Draft responses are evaluated for relevance, criteria definition, and evidence alignment. Press Ctrl/Cmd+Enter to post.
Select Support or Counterpoint to unlock structured response options.
Attachments
Images (5 MB), PDF (10 MB), audio (25 MB), video (100 MB).
Debate Tree
1 response — live from Firestore.
PN
Pat N.
TD: 75
AQ: 70
Initial Stance
Open-by-default can work if redaction standards are versioned and independently audited.
Evidence Type: No Evidence
Timestamp: 1d ago
0 Supports
0 Counterpoints
0 Evidence
0 OutcomesClaim Resolution Framework v2 — Argument Evaluation
Argument Quality and Claim Truth Status are evaluated independently.
Argument Quality ≠ Claim Truth
TheDebator rewards Reasoning quality. It does not automatic agreement with ai.
- • Good arguments can defend unpopular positions.
- • Bad arguments can defend popular positions.
- • A debater can score high on argument quality even when the claim is false.
Argument Quality (Independent)
72/100
Measures how well the debater argues — not whether the claim is true or popular.
Topic Discipline75
Reasoning Strength70
Counterpoint Engagement65
Evidence Relevance72
Steelmanning70
Structure71
Civility84
Claim Truth Status (Independent)
Subjective ClaimPending claim truth resolution.
Subjective Claim — No Winner Declared
Subjective claim — AI does not pick a winner.
Placeholder.
Standards
50
Reasoning
50
Evidence
50
Consistency
50
Burden of Proof (v1)
Burden assigned: Moderate. Burden met (81/100).
Burden Assigned: ModerateMet
Burden Met
81/100
Flags
Burden Avoided: No
Burden Shifted: No
- • Burden met for the assigned level of proof.
Steelman Framework (v1)
Steelman 70/100 · ModerateSteelman Score: 70/100 — Partial steelman; room to engage strongest view.
70/100
- • Continue separating understanding from critique.
Detailed Argument Analysis
Reasoning Strength
Moderate70/100
Claim → Reasoning → Evidence → Conclusion· Chain incomplete
- • Pending full text analysis
Placeholder reasoning evaluation.
AI Summary
Mock evaluation for Initial Stance — assesses reasoning quality, not whether the opinion is correct.
Topic Discipline
75/100
Legacy Quality Score
70/100
Topic Discipline
75/100
Reasoning Quality
70/100
Evidence Quality
68/100
Evidence Relevance
72/100
Counterpoint Engagement
65/100
Nuance & Context
68/100
Scope Accuracy
72/100
Placeholder — Argument Evaluation Framework will score this when connected.
Suggested Improvement
Define your standard, support with relevant evidence, and engage the strongest counterpoint.
Branch Reading Mode
Isolate one branch to read only that reasoning path.
Focused Path
Initial Stance → Mixed Paths