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Sprint projectSep 14, 2026Saudi Arabia

Whose Page Did You Count? Counting-Dependence and a Null Result in Measuring Attention to an AI Incident

Fatimah Emad Eldin · Team Baseline Drift

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Whose Page Did You Count? Counting-Dependence and a Null Result in Measuring Attention to an AI Incident

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Presentation: Whose Page Did You Count? Counting-Dependence and a Null Result in Measuring Attention to an AI Incident

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How much attention did an AI incident get? The answer depends on a choice nobody states: which page you count. Re-measuring the July 2026 OpenAI/Hugging Face intrusion, the published finding that June’s export-control ban drew roughly seven times more attention inverts once the page that absorbed the attention is counted instead of the developer’s: the breached party’s page outdrew every June comparator tested, in both raw and baseline- normalised units. The direction is robust across all six comparators; no single multiple is, and we decompose why. Second, a season-matched placebo null puts the 30-day response of AI-risk concept pages at the 53.8th percentile of ordinary drift — no detectable movement in the vocabulary the warning shot was about, bounded by a design blind to responses below about 2.2× drift. Third, a cross-channel decay comparison that looks headline-grade does not survive its null: the incident’s discussion thread sits at the 33rd percentile of thirty comparable front-page threads, so that half-life measures the platform and not the event. Code, cached fixtures and a verify.py that recomputes every number accompany the report.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.

  1. The paper convinces its readers that there was no significant media coverage of the OpenAI Hugging Face incident. It is transparent and provides what the author's assumed happended. The core limitation is that everything here is n=1: the practical recommendations are hypotheses drawn from a single event, and the paper is explicit that they should be read that way.

Cite this project

@misc{eldin2026whose,
  title = {{Whose Page Did You Count? Counting-Dependence and a Null Result in Measuring Attention to an AI Incident}},
  author = {Fatimah Emad Eldin},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/whose-page-did-you-count-countingdependence-and-a-null-result-in-measuring-attention-to-an-ai-incident-xyi6}},
  url = {https://apartresearch.com/sprints/projects/whose-page-did-you-count-countingdependence-and-a-null-result-in-measuring-attention-to-an-ai-incident-xyi6}
}

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