# Michael Zhang, PhD (@michael zhang, phd) on linkedin

- id: koinon:creator:linkedin_michael zhang, phd
- url: https://koinonlink.com/kol/linkedin/michael%20zhang%2C%20phd
- schema_version: 1.4

## Identity
- platform: linkedin
- handle: michael zhang, phd
- platform_url: https://www.linkedin.com/posts/drmikezhang_responsible-gambling-and-recovery-get-treated-activity-7468300603228700672-Fynr
- entity_type: creator
- person_id: Cross-platform identity not resolved for this creator.

## Reach
- posts_observed: 1
- verified: false
- size_tier: nano

## Engagement
- status: not_measurable_on_platform
- status_note: This platform publishes no follower count, so an engagement RATE has no denominator here. No amount of further collection can produce one — it is a property of the platform, not a gap in our data.
- unit_note: engagement_rate and peak_post_engagement_rate are ratios (0-1); avg_interactions_per_post and best_post_interactions are absolute counts. Read status before reading any number: a null rate has several different causes and status says which one applies here.

## Content
- category: finance
- subcategory: crypto
- category_confidence: 0.85
- category_source: derived_from_recent_content
- topics: Responsible gambling limitations, Gambling recovery vs. harm reduction, Systemic issues in gambling safety models
- bio: Responsible gambling and recovery get treated as the same thing. They aren't. They're built around fundamentally different questions. Responsible gambling asks: "How can gambling continue safely?" Recovery asks: "How can gambling become unnecessary?" Responsible gambling focuses on reducing harm whi

## Audience (inferred)
- status: present
- description: People interested in gambling addiction recovery and critiques of responsible gambling policies (likely recovery advocates, policy professionals or affected individuals)
- roles: none
- seniority: practitioner
- pain_points: Systems that make setting limits and staying in control nearly impossible, Conflation of responsible gambling messaging with actual recovery support, Lack of distinction between harm-reduction models and true recovery frameworks
- posts_analysed: 2
- confidence: 0.4

## Commercial (inferred)
- audience_buyer_fit: 1
- promotion_intent: 0.7
- open_to_collaboration: false
- contact: Koinon does not publish creator contact details.

## Quality signals
- authenticity_score: 60
- authenticity_basis: platform_default
- flagged_for_review: false
- koinon_bd_grade: A — measures Koinon partner-outreach fit, not creator quality
- interpretation: Signals for triage, not verdicts. A flag means the profile is worth a manual check.

## Evidence
- [linkedin_michael zhang, phd#d86923bd] We tell people to set limits and stay in control inside systems engineered to make that impossible. That's the core problem with responsible gambling. The model assumes people stay rational and consistent. But once someone is chasing losses, the brain isn't operating rationally.  (https://www.linkedin.com/posts/drmikezhang_responsible-gambling-and-recovery-get-treated-activity-7468300603228700672-Fynr)
- [linkedin_michael zhang, phd#51fae2df] Responsible gambling and recovery get treated as the same thing. They aren't. They're built around fundamentally different questions. Responsible gambling asks: "How can gambling continue safely?" Recovery asks: "How can gambling become unnecessary?" Responsible gambling focuses 

## Provenance
Every inferred field below carries how it was produced. Facts stay facts; inferences stay inferences.

- audience.description: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.roles: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- audience.seniority: source_type=ai_inferred, confidence=0.4, freshness_days=76, method=llm_persona_from_recent_content
- commercial.audience_buyer_fit: source_type=ai_inferred, confidence=0.6, freshness_days=76, method=llm_scorer — Relative signal within Koinon indexing, not a market-wide score.
- commercial.promotion_intent: source_type=ai_inferred, confidence=0.6, freshness_days=76, method=llm_scorer
- quality.anomaly_flags: source_type=heuristic, confidence=0.5, freshness_days=116, method=profile_snapshot_and_engagement_ratio_heuristic — Means "worth a manual check", NOT confirmed fraud. Flags can be wrong and are not a verdict about the person.
- quality.authenticity_score: source_type=heuristic, confidence=0.25, freshness_days=76, method=Fixed value 60. This platform exposes no public follower count, so no authenticity heuristic is run at all. The number is a placeholder, not a measurement. — The input set differs by platform; do not compare this score across platforms.
- quality.koinon_bd_grade: source_type=ai_inferred, confidence=0.5, freshness_days=116, method=llm_partner_fit_classifier + fit_score >= 0.5 => A; >= 0.35 => B; else C. authenticity is not an input. — Koinon partner-outreach fit, not creator quality.
- reach.followers: source_type=observed, confidence=0.95, freshness_days=116, method=platform_profile

## Freshness
- profile_checked_days_ago: 116
- content_analysed_days_ago: 76
- audience_updated_days_ago: 76

## AI retrieval
- indexing_requests: 4
- user_triggered_requests: 0
- note: Being indexed in bulk is not the same as being retrieved to answer a question.

## This profile
- Claim or correct: https://koinonlink.com/claim?k=linkedin_michael%20zhang%2C%20phd
- Request removal: https://koinonlink.com/claim?k=linkedin_michael%20zhang%2C%20phd
- Terms: https://koinonlink.com/for-ai