Skip to content
Navigation
🏠Overview
Knowledge
🔬Scientific Foundation
🧠Critical Thinking
🤖AI and Technology
Debunking
🔮Esotericism and Occultism
🛐Religions
🧪Pseudoscience
💊Pseudomedicine
🕵️Conspiracy Theories
Tools
🧠Cognitive Biases
✅Fact Checks
❓Test Yourself
📄Articles
📚Hubs
Account
📈Statistics
🏆Achievements
⚙️Profile
Deymond Laplasa
  • Home
  • Articles
  • Hubs
  • About
  • Search
  • Profile

Knowledge

  • Scientific Base
  • Critical Thinking
  • AI & Technology

Debunking

  • Esoterica
  • Religions
  • Pseudoscience
  • Pseudomedicine
  • Conspiracy Theories

Tools

  • Fact-Checks
  • Test Yourself
  • Cognitive Biases
  • Articles
  • Hubs

About

  • About Us
  • Fact-Checking Methodology
  • Privacy Policy
  • Terms of Service

Account

  • Profile
  • Achievements
  • Settings

© 2026 Deymond Laplasa. All rights reserved.

Cognitive immunology. Critical thinking. Defense against disinformation.

  1. Home
  2. /Pseudomedicine
  3. /Folk Medicine vs. Evidence-Based Medicine
  4. /Folk Medicine vs Evidence-Based Medicine
  5. /Pseudoscience in Medicine: How to Recogn...
📁 Folk Medicine vs Evidence-Based Medicine
⚠️Ambiguous / Hypothesis

Pseudoscience in Medicine: How to Recognize Dangerous Recommendations and Protect Yourself from Manipulation — PMC Data Analysis

Pseudoscientific practices in medicine pose a real threat to health by masquerading as scientific methods. The absence of large public datasets with patient annotations hinders the development of clinical decision support systems. PMC-Patients — the largest resource with 167,000 patient summaries and 3.1 million relevance annotations — opens new opportunities to combat pseudoscience through data-driven approaches. Critical thinking and understanding levels of evidence remain key tools for cognitive defense.

📅
Published: February 2, 2026
⏱️
Reading time: 9 min

Neural Analysis

Neural Analysis
  • Topic: Pseudoscience in medicine, recognition methods, role of PMC data in clinical decision support
  • Epistemic status: Moderate confidence — PMC-Patients data confirmed by publication in Scientific Data (2023), but direct research on effectiveness against pseudoscience is limited
  • Evidence level: Observational studies, technical datasets, methodological guidelines; large RCTs on the impact of data-driven systems on reducing pseudoscientific practices are absent
  • Verdict: Pseudoscience in medicine is dangerous due to replacing evidence base with emotional narratives. Large-scale datasets like PMC-Patients create infrastructure for objective evaluation of clinical recommendations, but require further validation in real-world practice.
  • Key anomaly: Most pseudoscientific methods exploit cognitive biases (confirmation, availability) and lack of public data for independent verification
  • Check in 30 sec: Ask: "Where is the patient data published that confirms effectiveness?" — if there's no answer, that's a red flag
Level1
XP0

Pseudoscientific practices in medicine pose a real threat to health by masquerading as scientific methods. The absence of large public datasets with patient-level annotations has hindered the development of clinical decision support systems. PMC-Patients—the largest resource with 167,000 patient summaries and 3.1 million relevance annotations—opens new possibilities for combating pseudoscience through data-driven approaches. Critical thinking and understanding levels of evidence remain key tools of cognitive defense.

🖤 In an era of information abundance, the boundary between scientific medicine and pseudoscientific practices is becoming increasingly blurred for the average person. Every day, millions of people make decisions about their health based on recommendations that appear convincing but lack scientific foundation. 👁️ Pseudoscience in medicine is not merely a harmless misconception: it is a systemic threat that exploits human cognitive vulnerabilities, masquerades as legitimate science, and can lead to tragic consequences. The problem is compounded by the fact that the development of effective clinical decision support systems (ReCDS) has long faced a critical obstacle—the absence of large-scale public datasets with patient-level annotations (S004). 💎 The emergence of PMC-Patients, containing 167,000 patient summaries and 3.1 million relevance annotations, opens a new era in combating medical misinformation through data-driven approaches (S004).

📌 What Constitutes Pseudoscience in Medicine and Why It's So Difficult to Distinguish from Real Science

⚠️Defining Pseudoscience: When Imitation of Scientific Method Becomes a Weapon of Manipulation

Pseudoscience in medicine is a collection of practices, claims, and treatment methods that present themselves as scientifically grounded but do not meet the criteria of the scientific method. The key difference between pseudoscience and outright quackery lies in its mimicry: it uses scientific terminology, references studies (often misinterpreting them), creates the appearance of an evidence base, and exploits the authority of science to legitimize unverified or refuted methods. More details in the section Essential Oils as a Panacea.

Critical thinking and understanding the differences between science and pseudoscience are fundamental skills for protection against manipulation (S005).

🧩Structural Characteristics of Pseudoscientific Medical Practices

Pseudoscientific medical practices possess a number of characteristic features that allow their identification upon careful analysis.

Anecdotal Evidence Instead of Controlled Studies
Personal success stories create an illusion of effectiveness but do not control variables and do not exclude the placebo effect.
Absence of Falsifiability
Claims are formulated so they cannot be empirically refuted—any result is interpreted as confirmation.
Appeal to Antiquity and "Naturalness"
Positioning itself in opposition to "conventional medicine" creates a false dichotomy: old doesn't mean effective, new doesn't mean harmful.

🔎The Boundary Between Innovation and Pseudoscience: Why Context Matters

Not every unverified or alternative practice is automatically pseudoscience. Medical science constantly evolves, and many modern methods were once experimental.

Innovative Medicine Pseudoscience
Open to testing and criticism Resists testing, accuses science of conspiracy
Publishes results in peer-reviewed journals Avoids peer review or publishes in low-quality sources
Acknowledges limitations and willing to abandon hypothesis Ignores contradictory data, reformulates theory when criticized

Medical history knows examples where pseudoscientific theories, such as phrenology, served as warnings for modern disciplines (S003).

🧱Systemic Problem: Why Lack of Data Strengthens Pseudoscience

One factor contributing to the spread of pseudoscience in medicine is the shortage of accessible, quality data for developing clinical decision support systems. The development of clinical decision support systems based on information extraction has long faced a serious obstacle: the absence of diverse patient collections and publicly available large-scale datasets with patient-level annotations (S004).

An information vacuum creates a space in which pseudoscientific claims flourish. The absence of systematized data makes rapid verification of medical claims and recommendations difficult.

When a physician or patient cannot quickly access a reliable evidence base, they become vulnerable to convincingly formulated but unfounded recommendations. This is especially dangerous in rare diseases, where data is inherently scarce, and in areas where research is insufficiently funded.

Visualization of structural characteristics of pseudoscientific practices in medicine
Schematic representation of the main features of pseudoscientific medical practices: absence of falsifiability, reliance on anecdotal evidence, resistance to testing, and exploitation of scientific terminology

🧪The Strongest Arguments for Alternative Medical Practices: Why People Believe in Them

⚠️ The Placebo Effect as a Real Physiological Phenomenon Exploited by Pseudoscience

Patients genuinely feel improvement after alternative practices — this is not an illusion. The placebo effect is a measurable physiological response: endorphin release, changes in brain activity, influence on the immune system. More details in the section Extreme Diets and Miracle Cures.

Pseudoscientific methods maximize placebo through rituals, patient attention, and an atmosphere of care. The trap: the placebo effect does not treat the underlying disease and masks the progression of serious conditions.

🧩 Natural Regression to the Mean: When Improvement Happens on Its Own

Many diseases have a cyclical nature or natural tendency toward improvement. Patients seek help at the peak of symptoms — subsequent improvement is often the result of natural recovery, not the effectiveness of the method.

Pseudoscientific practices receive undeserved credit when natural recovery is attributed to their effectiveness. This is especially pronounced with self-limiting conditions: colds, minor injuries, temporary pain.

🕳️ Personalized Approach and Emotional Support: What Is Truly Valuable in Alternative Medicine

Alternative practices offer what conventional medicine lacks: time and attention. Consultations last an hour or more, while a doctor's appointment is 10–15 minutes.

This care has real therapeutic value, especially for chronic conditions. The problem: when emotional support is combined with ineffective or dangerous methods, patients abandon proven approaches.

⚠️ Shortcomings of Conventional Medicine as a Catalyst for Turning to Alternatives

Conventional medicine has real problems: high cost, inaccessibility, bureaucracy, drug side effects, diagnostic errors, lack of empathy.

Pseudoscientific practices position themselves as the solution, offering "natural," "safe," and "holistic" approaches. This narrative is especially attractive to disillusioned patients.

🧠 Cognitive Biases and Heuristics: Why Our Brains Are Predisposed to Believe in Pseudoscience

The brain evolved for quick decisions under uncertainty, leading to cognitive heuristics — mental shortcuts that often work but systematically err.

  1. Availability heuristic: overestimating the probability of events recently heard about or easily recalled
  2. Confirmation bias: seeking information that confirms existing beliefs
  3. Halo effect: trusting a charismatic person or someone with impressive credentials, even without evidence

Pseudoscientific practices exploit these vulnerabilities systematically and effectively.

🔁 Social Proof and Authority: Mechanisms for Spreading Pseudoscientific Beliefs

People are social creatures. The principle of social proof: behavior is considered correct if many others demonstrate it. When celebrities, bloggers, and friends recommend alternative practices, powerful social pressure is created.

Mechanism How It Works Result
Social proof We see many people believe and use the method We consider it legitimate
Appeal to authority Titles, degrees (often from unrecognized institutions), appearance of expertise Trust without verifying evidence
Self-reinforcing cycle Authority + social proof together Exponential spread of beliefs

💎 Economic Interests and Marketing: The Alternative Medicine Industry as a Business Model

Alternative medicine is a multi-billion dollar industry with powerful economic incentives. Unlike pharmaceutical drugs, many alternative remedies do not require rigorous clinical trials and are sold with minimal regulation.

The asymmetry: producers of alternative remedies make bold claims about effectiveness without needing to provide convincing evidence. Marketing uses emotional appeals, success stories, and fear of "chemicals" and "Big Pharma."

Economic interests create incentives to continue promoting pseudoscientific practices, even as evidence of their ineffectiveness accumulates.

🔬Evidence Base and Critical Analysis: What the Data Says About Pseudoscience in Medicine

📊 PMC-Patients: Revolution in Medical Data Accessibility for Fighting Misinformation

PMC-Patients is the largest resource for developing clinical decision support systems. It contains 167,000 patient summaries with 3.1 million patient-article relevance annotations and 293,000 patient-patient similarity annotations (S004).

Human evaluation confirms the high quality of annotations and dataset diversity (S004). This opens the possibility of creating systems that rapidly verify medical claims and identify pseudoscientific recommendations through comparison with validated clinical cases.

🧾 Evaluating ReCDS Systems: Current Challenges and Development Prospects

The PMC-Patients benchmark shows that creating effective automated systems for identifying pseudoscientific claims remains a complex task (S004). Pseudoscience often uses scientific terminology, references real studies, but interprets them incorrectly or selectively.

Systems must not only match symptoms and diagnoses, but also assess evidence quality, identify logical fallacies, and recognize manipulative rhetorical techniques. More details in the Detox Myths section.

System Task Complexity Why It's Critical
Matching symptoms and diagnoses Medium Basic function, but insufficient
Assessing evidence quality High Separates science from pseudoscience
Identifying logical fallacies High Trap: correlation ≠ causation
Recognizing manipulative rhetoric Very high Requires context and semantics

🔎 Data and Code Accessibility: Open Science as a Tool Against Pseudoscience

PMC-Patients adheres to open science principles: the dataset and code are publicly available (S004). This allows researchers to develop their own systems for verifying medical claims.

Data and methodology openness is the antithesis of pseudoscience, which conceals methods, doesn't publish raw data, and resists independent verification.

The availability of such resources democratizes the ability to verify medical claims and creates infrastructure for collective combat against medical misinformation. This is especially important in the context of psychosomatic myths, where the boundary between real effect and suggestion is often blurred.

🧪 Integration with PubMed Central: Medical Knowledge Ecosystem

PMC-Patients is embedded in the PubMed Central ecosystem—a free full-text archive of biomedical literature. Integration of various sources, including Royal Society of Medicine publications now deposited in PMC (S008), creates a comprehensive knowledge base.

ReCDS systems can now not only match clinical cases but also link them to primary scientific publications, ensuring evidence traceability from specific patients to original research.

📊 Mathematical and Statistical Methods in Systems Medicine: From Data to Understanding

Fighting pseudoscience requires applying sophisticated mathematical and statistical methods. Mathematical techniques are particularly useful for studying signaling pathways, such as the Wnt pathway (S006).

Predictive Models
Allow not only describing biological processes but also predicting intervention effects—critical for separating real therapeutic effects from placebo or natural regression.
Pattern Analysis
Applying statistical methods to PMC-Patients data helps identify characteristic features of pseudoscientific claims and create algorithms for their automatic detection.
Systems Medicine
Integrates multiple data sources to build a holistic picture of disease, making manipulation of individual facts more difficult.

A review of such methods serves as a starting point for analyzing models in systems medicine (S006). Applying these approaches to real clinical data creates an objective foundation for distinguishing evidence-based medicine from its imitations.

Visualization of the PMC-Patients ecosystem and its role in fighting medical misinformation
PMC-Patients ecosystem diagram: 167,000 patient summaries, 3.1 million relevance annotations, integration with PubMed Central and clinical decision support systems

🧬Mechanisms of Influence: How Pseudoscience Exploits Human Biology and Psychology

🧠 The Neurobiology of Belief: Why the Brain Prefers Simple Explanations to Complex Ones

The human brain evolved to process information efficiently with limited cognitive resources. This led to a preference for simple, intuitive explanations over complex multifactorial models. Learn more in the Cognitive Biases section.

Pseudoscientific practices exploit this feature by offering simple cause-and-effect relationships: "toxins cause all diseases," "energy blockages disrupt health." Neuroimaging studies show that accepting simple explanations activates reward centers in the brain, creating positive reinforcement for pseudoscientific beliefs.

The brain rewards itself for simplicity. This isn't an evolutionary bug — it's a compromise between decision speed and accuracy under uncertainty.

🔁 Causality vs. Correlation: The Fundamental Error of Pseudoscientific Thinking

One of the most common logical errors in pseudoscientific claims is conflating correlation with causality. Two events occurring simultaneously or sequentially doesn't mean one causes the other.

Pseudoscientific practices systematically interpret any improvement after applying a method as proof of its effectiveness. Establishing causality requires controlled experiments, accounting for confounders, and reproducible results.

Correlation Causality
Two events are statistically linked One event directly causes another
May be coincidental Requires mechanism and control
Observed in data Proven by experiment

🧷 Confounders and Systematic Errors: Why Observational Studies Can Mislead

Confounders are variables linked to both the supposed cause and effect, creating a false appearance of causation. People using alternative medical practices often lead healthier lifestyles, eat better, and exercise more.

Any health improvement may result from these factors rather than the alternative practice itself. Randomized controlled trials are specifically designed to minimize confounders, but pseudoscientific practices rarely undergo such testing.

Selection Bias
People choosing alternative methods differ from control groups across multiple parameters unrelated to the method itself.
Placebo Effect
Expectation of improvement activates real physiological mechanisms, independent of any active treatment component.
Natural Regression
Many conditions improve on their own over time; coinciding with treatment creates an illusion of effectiveness.

🧬 Biological Variability and Individual Differences: Why "Works for Me" Isn't Evidence

People demonstrate significant biological variability in responses to treatment, placebo, and natural disease progression. This variability means some people will experience improvement regardless of what treatment they receive, simply through statistical chance.

Pseudoscientific practices collect and publish success stories while ignoring failures. Personal experience is the weakest form of evidence because it doesn't control for placebo effects, natural regression, confounders, or systematic errors.

"Works for me" is an observation, not proof. Only large-scale controlled studies separate real effects from the noise of biological variability.

Understanding these mechanisms is critical for protection against manipulation. When you see claims about miracle cures, ask yourself: were confounders controlled, was there a control group, are results reproducible. Lack of answers to these questions is a red flag.

⚠️ Data Conflicts and Zones of Uncertainty: Where Science

⚔️

Counter-Position Analysis

Critical Review

⚖️ Critical Counterpoint

Technological solutions and access to data are necessary but insufficient tools against pseudoscience. Real barriers lie in the social, psychological, and organizational dimensions, which algorithms do not solve.

Overestimation of the Role of Data

The article creates the impression that large datasets like PMC-Patients will automatically solve the pseudoscience problem—this is technological determinism. Pseudoscience thrives not due to lack of data, but because of social, psychological, and economic factors: distrust of institutions, financial motivation of pseudotreatment sellers, emotional vulnerability of patients. Data is a necessary but insufficient condition.

Pseudoscience Adaptability Outpaces Detection

The article assumes that ReCDS systems will effectively identify pseudoscientific claims, but pseudoscience adapts quickly: "studies" with fabricated data are already emerging, publications in predatory journals with peer-review imitation, use of AI to generate plausible but false scientific texts. The arms race between detection and deception may make technical solutions less effective than expected.

Gap Between Specialist Access and Patient Vulnerability

PMC-Patients and ReCDS systems are accessible primarily to specialists and researchers, not to end patients who are most vulnerable to pseudoscience. The article does not account for the scientific literacy barrier: even with data available, most people cannot correctly interpret it without specialist assistance.

Ethical Risks of Automation and Bias Inheritance

The article is insufficiently critical of ReCDS system risks: algorithms inherit bias from training data (for example, underrepresentation of certain demographic groups in PMC-Patients), leading to inequality in recommendation quality. Moreover, excessive trust in automated systems may reduce physicians' critical thinking.

Time Lag Between Data Updates and Knowledge Changes

Medical recommendations change faster than datasets and models are updated. An article written based on 2023 data may already contain outdated assessments of ReCDS system effectiveness if new methods have emerged or critical limitations of PMC-Patients have been discovered during this time.

Knowledge Access Protocol

FAQ

Frequently Asked Questions

Pseudoscience in medicine refers to practices and claims that masquerade as scientific methods but lack an evidence base, reproducible results, or scientific validation. Such methods often exploit patients' cognitive biases (fear, hope, distrust of conventional medicine) and use scientific-sounding terminology to create an illusion of legitimacy. Examples include homeopathy in doses without active ingredients, "energy healing" without a physical mechanism of action, and unsubstantiated cancer diets. The key difference from science: pseudoscience does not change its claims when contradictory data emerges and avoids independent verification.
PMC-Patients provides objective infrastructure for verifying clinical claims through large-scale data. The dataset contains 167,000 patient summaries with 3.1 million patient-article relevance annotations and 293,000 patient-patient similarity annotations (S004). This enables development of Retrieval-based Clinical Decision Support systems (ReCDS) that can automatically match patient cases with validated scientific publications, identify discrepancies between recommendations and evidence base, and train models to recognize patterns of pseudoscientific claims. Human evaluation confirmed high annotation quality (S004), which is critical for reliable conclusions.
People believe due to a combination of cognitive biases and information deficits. Main mechanisms: (1) Confirmation bias—people remember instances of "improvement" after pseudotreatment while ignoring deterioration or natural recovery. (2) Availability heuristic—vivid stories of "miraculous healings" are remembered better than statistics of ineffectiveness. (3) Illusion of control—pseudoscience provides a sense of active agency in situations of helplessness against disease. (4) Distrust of "conventional medicine" due to negative experiences or conspiratorial narratives. (5) Lack of access to quality data—before resources like PMC-Patients, patients couldn't independently verify claims (S004 notes that ReCDS system development was "severely obstructed by the lack of diverse patient collections").
Key red flags: absence of publications in peer-reviewed journals, claims of "miraculous" effects without mechanism of action, references to "ancient wisdom" instead of clinical trials, promises to cure a wide range of unrelated diseases with one method, aggressive criticism of "conventional medicine" without constructive alternatives, payment required before providing evidence, refusal of double-blind trials under the pretext of "individualized approach," use of testimonials instead of statistics, appeal to authority of "doctors" whose credentials aren't verified, absence of data on side effects (any active intervention has risks).
Levels of evidence are a hierarchical system for assessing the reliability of medical data. Highest level (Grade A): systematic reviews and meta-analyses of randomized controlled trials (RCTs)—multiple quality studies with reproducible results. Level B: individual RCTs with adequate sample size. Level C: observational studies (cohort, case-control)—show correlations but don't prove causation. Level D: case series, expert opinions—weakest level, subject to biases. Pseudoscience typically relies on Level D or has no data at all, but presents itself as Level A through manipulation of terminology.
No, PMC-Patients is not intended for patient self-diagnosis. It is a research dataset for developing and testing Retrieval-based Clinical Decision Support (ReCDS) systems used by medical professionals (S004). The dataset contains de-identified case summaries and relevance annotations but does not replace clinical expertise. Self-interpretation of medical data without professional training can lead to erroneous conclusions due to misunderstanding of context, comorbidities, and individual factors. Proper use: a physician applies an ReCDS system trained on PMC-Patients to search for relevant studies and similar cases, then integrates this information with clinical assessment of the specific patient.
Absence of public large-scale datasets creates an information vacuum filled by unreliable data. Without access to validated patient case collections (S004 points to "lack of diverse patient collections and publicly available large-scale patient-level annotation datasets"), it's impossible to: (1) Independently verify claims about treatment effectiveness. (2) Train algorithms to recognize disease patterns and treatment responses. (3) Compare a new case with thousands of similar ones to assess prognosis. (4) Identify systematic errors in recommendations. Pseudoscience exploits this vacuum by offering "exclusive data" or "secret studies" that can't be verified. PMC-Patients with its 167k summaries and millions of annotations (S004) closes part of this vacuum, making verification possible.
Main exploited biases: (1) Post hoc ergo propter hoc—"after this, therefore because of this": person took pseudomedicine and recovered, so it must have helped (ignoring natural disease course). (2) Survivorship bias—only "successful" cases are visible, failures are hidden. (3) Anchoring—first information heard (often from charismatic "healer") becomes the evaluation anchor. (4) Sunk cost fallacy—after investing money and time in pseudotreatment, person continues believing to avoid admitting error. (5) Appeal to nature—"natural = safe and effective" (ignoring that poisons are also natural). (6) Dunning-Kruger effect—lack of medical knowledge creates illusion of understanding after reading a few pseudoscientific articles.
Ask your doctor specific questions: (1) "What research is this recommendation based on?"—request journal names, authors. (2) "What level of evidence supports this data?"—a physician practicing evidence-based medicine knows the hierarchy. (3) "Are there systematic reviews or meta-analyses for this method?"—this is the highest level of evidence. (4) "What alternative approaches exist and why was this one chosen?"—assessment of comparative effectiveness. (5) "What are the side effects and how often do they occur?"—absence of risk information = red flag. Verify recommendations through PubMed, Cochrane Library, UpToDate. If a doctor refuses to discuss the evidence base or reacts aggressively to questions—that's a warning sign.
Don't attack beliefs directly—this activates defense mechanisms and the backfire effect (strengthening of belief when challenged). Strategy: (1) Ask Socratic questions: "Why do you think this method isn't studied at major universities?", "What could convince you the method doesn't work?". (2) Suggest checking data together—find studies in PubMed, show absence of evidence. (3) Discuss cognitive biases in neutral context (not about their case)—raise awareness of deception mechanisms. (4) Find compromise: "Let's continue standard treatment alongside this method and compare results". (5) Turn to an authority the person trusts (another doctor, scientist). (6) If there's health risk—involve a professional psychologist specializing in destructive beliefs.
PMC-Patients is a benchmark dataset, and its evaluation showed that the task remains challenging (S004: «evaluation of various ReCDS systems shows that the PMC-Patients benchmark is challenging and calls for further research»). Problems include: (1) Complexity of medical language — models may misinterpret clinical terminology, synonyms, and contextual nuances. (2) Rare diseases — insufficient training data for uncommon conditions. (3) Comorbidities — patients with multiple diseases create combinatorial complexity. (4) Knowledge dynamics — medical guidelines change, requiring constant model updates. (5) Ethical constraints — systems should not replace physicians, but the boundary between "support" and "replacement" is blurred. (6) Annotation quality — despite high ratings (S004), human annotators can make errors or introduce bias.
No, complete eradication is impossible for structural reasons. Pseudoscience exploits fundamental features of human psychology (cognitive biases, need for control, fear of death) that cannot be "switched off." Additionally, information asymmetry between specialists and patients creates space for manipulation. A realistic goal is minimizing influence through: (1) Improving public scientific literacy. (2) Accessibility of verified data (like PMC-Patients). (3) Regulatory measures against false advertising. (4) Developing critical thinking as a cultural norm. (5) Improving communication between physicians and patients — many turn to pseudoscience due to dissatisfaction with conventional medicine. Pseudoscience will evolve, adapting to new technologies (e.g., using AI-generated "studies"), so combating it requires constant adaptation of verification methods.
Deymond Laplasa
Deymond Laplasa
Cognitive Security Researcher

Author of the Cognitive Immunology Hub project. Researches mechanisms of disinformation, pseudoscience, and cognitive biases. All materials are based on peer-reviewed sources.

★★★★★
Author Profile
Deymond Laplasa
Deymond Laplasa
Cognitive Security Researcher

Author of the Cognitive Immunology Hub project. Researches mechanisms of disinformation, pseudoscience, and cognitive biases. All materials are based on peer-reviewed sources.

★★★★★
Author Profile
// SOURCES
[01] Native American Stories as Scientific Investigations of Nature: Indigenous Science and Methodologies

💬Comments(0)

💭

No comments yet