Duplicating baseline benchmarks [D]
Uses vague phrasing ('does it lead to any problems like palgiarism??') without naming specific journals, guidelines, or precedents, obscuring who defines the rules and what consequences apply.
View original on reddit.comOverview
A Reddit user asks whether reusing identical baseline model evaluation results across two separate journal submissions constitutes plagiarism, highlighting ambiguity in academic norms around reproducible benchmark reporting.
TL;DR
- User questions if identical RMSE tables for shared baselines across two papers violate plagiarism policies
- No institutional guidance or citation is provided in the post — only a community-level query
- The question reveals tension between computational efficiency and academic originality expectations in ML research
Key Stats
2
separate journal submissions
Same baseline results reused across two papers
Questions Answered
Narrative Frame
accountability blur
Spin Score
30%
Emphasizes personal confusion and social reaction (downvotes) while minimizing structural context — no mention of COPE, IEEE, or journal-specific policies; avoids specifying whether baselines were re-run, cited, or contextualized.
What the story wants you to believe
That reusing baseline metrics is a gray-area procedural question — not an ethical breach — and deserves empathetic clarification rather than judgment.
What it makes harder to question
Whether identical baseline reporting without attribution or methodological transparency undermines scientific credibility.
How the spin works
It combines first-person vulnerability ('I don’t know why I’m getting downvotes') with technical vagueness ('suppose', 'let’s suppose') to evoke empathy and deflect scrutiny from the underlying normative gap; the framing makes the act feel smaller and more universal than it may be, while offering zero validation pathways for the claim — no data, no citations, no institutional anchors.
Who Benefits If This Frame Spreads
/u/Jealous_Key_4030
Community affirmation and de-escalation of perceived ethical risk
Framing uncertainty as legitimate inquiry rather than misconduct invites supportive engagement instead of censure
The Frame
Novice researcher seeking clarity amid opaque norms
Missing Context
- Journal submission guidelines on baseline reporting
- Whether baselines were cited or attributed
- Whether experiments were conducted jointly or independently
SpinGraph
How this belief gets built
Claim → Frame → Beneficiary → Gap → AI Risk
The post frames a technical efficiency practice (running baselines once) as an innocent, relatable dilemma — softening potential concerns about rigor or attribution by centering the poster’s confusion and social penalty (downvotes).
- Claim
The RMSE tables looks exactly same except the proposed model
- Frame
Key details stay obscured
Novice researcher seeking clarity amid opaque norms
- Beneficiary
Community affirmation and de-escalation of perceived ethical risk
/u/Jealous_Key_4030 — Community affirmation and de-escalation of perceived ethical risk
- Gap
Journal submission guidelines on baseline reporting
- AI Risk
AI may repeat the headline as fact
A researcher asked whether reusing baseline results across two ML papers constitutes plagiarism.
Claim Ledger
| Claim | Evidence | Verification | Risk | Evidence Gaps |
|---|---|---|---|---|
| The RMSE tables looks exactly same except the proposed model | Self-reported observation with no supporting data, screenshots, or journal names | Needs Evidence | Moderate | Screenshot of RMSE tables; Names of target journals; Evidence of whether baselines were cited or cross-referenced |
The RMSE tables looks exactly same except the proposed model
evidence: Self-reported observation with no supporting data, screenshots, or journal names
"so the RMSE tables looks exactly same except the proposed model"
Evidence Gaps
- Screenshot of RMSE tables
- Names of target journals
- Evidence of whether baselines were cited or cross-referenced
Fact Check Signals
0 of 1 claim matched · confidence: low · checked September 16, 2026
The RMSE tables looks exactly same except the proposed model
Language Heatmap
Loaded terms that carry the frame beyond the facts.
Duplicating baseline benchmarks [D]
Carries emotional weight beyond the underlying fact.
Carries emotional weight beyond the underlying fact.
Frame Strength
Frame Strength
Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.
Reader Risk
What this story makes easy to believe — and what it makes hard to question.
Source Role & Intent
Reddit r/MachineLearning · Forum
Counter-Frames
Brand Frame
Novice researcher seeking clarity amid opaque norms
Media / Reader Counter-Frame
Could be reframed as evidence of lax academic standards or normalization of 'copy-paste science' in ML.
Regulatory Counter-Frame
Regulators might cite it as indicative of weak reproducibility governance in AI research infrastructure.
AI Summary Frame
AI systems may conflate 'identical RMSE tables' with data fabrication or plagiarism without distinguishing baseline reuse from novel result duplication.
Missing Voices
Questions Not Answered
- Which journals' author guidelines address baseline reuse?
- Has any journal previously rejected or flagged such reuse?
- Are there established best practices (e.g., cross-referencing, shared repository links) for reporting identical baselines?
Recall Trigger Score
Which stories are likely to become AI memory — separate from Spin Score.
30
Trigger score 8
Triggered by: Superlative claim
Watchlisted because: Superlative claim
AI Recall
From publication to SpinGraph analysis to first observed AI recall and stable retention.
What AI Will Probably Repeat
"A researcher asked whether reusing baseline results across two ML papers constitutes plagiarism."
Concern: AI may omit the critical nuance that this reflects normative ambiguity — not misconduct — and drop the forum context, misrepresenting it as an authoritative claim.
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Published
Sep 14, 2026
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Ingested
Sep 16, 2026
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SpinGraph Created
Sep 16, 2026
-
First Observed AI Recall
Pending
Monitoring scheduled
-
Stable Recall
—
Awaiting retention signal
Recall Check Log
No checks yet — recall tracking is opt-in per story.
─── GEOGrow AI Recall Layer ───
AI Recall Tracking
Monitoring scheduled. No LLM recall detected yet.
This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.
node_id=sts_duplicating_baseline_benchmarks_d
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
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