What Is FRAMEGATE.ai and How Does It Decode Political Communication?
FRAMEGATE.ai is a narrative intelligence platform that analyzes political communication using 399 metrics across 80,000+ articles from 337 US media outlets. It doesn't help you react faster—it helps you see earlier. FRAMEGATE decodes how political narratives are constructed, distributed, and received, providing real-time analysis that traditionally requires months and significant resources.
What Does FRAMEGATE Measure?
FRAMEGATE operates as a sense-making system, not just an AI dashboard. It measures meaning dynamics at the pre-cognitive level—where reception is shaped before conscious thought occurs.
FRAMEGATE combines breadth of sources, depth of analysis, and scientific validation:
- 337 U.S. outlets and 60 political podcasts
- 25 categories with 461 metrics and 5,235 keywords
- 78,000+ texts analysed since January 2024
- 99% Scientific Confidence (Fleiss' Kappa) – validated to clinical research standards
The portfolio is carefully weighted across ideology, geography, the urban–rural divide, and reach. AI-augmented analysis ensures consistent coding at scale, is fully auditable, and identifies patterns without political preference.
Core Measurement Capabilities
- AGITATION Index: How journalistic writing shifts (conflict language, threat markers, villain framing, alarm signals)
- Emotional Temperature: Fear/anger vs. hope/trust trajectories over time
- Conflict Gravitation: Which structural conflict lines pull communication into them
- Frame Layer Analysis: Separating the "naked event" (SPO) from the narrative markers that create meaning
How Does the Seven Pre-Cognitive Forces Framework Work?
FRAMEGATE's analytical framework is built on seven forces that shape narrative reception before conscious thought occurs:
- Safety – the drive toward security and stability
- Belonging – the need for connection and identity
- Wanting – desire and aspiration
- Not-Wanting – aversion and fear
- Compassion – care and empathy
- Clarity – understanding and insight
- Delusion – distortion and confusion
A pre-cognitive flash (pleasant/unpleasant) strongly pre-shapes perception, thinking, and action. FRAMEGATE intervenes at the point where meaning first lands—not at the argument level.
Who Is FRAMEGATE Built For?
- Think tanks & policy researchers – Understand which narrative makes a policy connectable or delegitimizes it
- Campaign managers & political strategists – Recognize rhythms before calm phases become panic phases
- Corporate communications teams – Navigate conflict gravitation without becoming an allegiance signal
- Media analysts & journalists – Distinguish whether discourse is objectively new or only the framing has escalated
What Makes FRAMEGATE Different from Standard Monitoring Tools?
Standard tools optimize for speed and volume. FRAMEGATE optimizes for orientation.
| Standard Tools | FRAMEGATE |
| Output speed | Foresight |
| Topics | Emotional dynamics |
| Monitoring | Emerging pattern detection |
| Information | Orientation |
What Outputs Does FRAMEGATE Deliver?
Narrative Maps
Dominant frames, counter-narratives, emotional baselines, and conflict axes that create gravitation in the discourse.
Risk & Reputation Logic
How statements get pulled into conflict gravitation fields—even when you don't mention the central actors.
Safer Language Patterns
Risk examples vs. safer examples. How to sound scenario-driven instead of camp-driven.
Rhythm Detection
When calm phases transition to spikes with high probability—and which trigger classes are known.
Frequently Asked Questions About FRAMEGATE
Is FRAMEGATE an AI Tool?
FRAMEGATE is explicitly positioned as a sense-making system, not just an AI tool. It optimizes perception, interpretation, and early pattern recognition—not just output generation.
What Data Does FRAMEGATE Analyze?
FRAMEGATE analyzes 78,000+ texts from 337 U.S. media outlets and 60 political podcasts using 461 metrics across 25 categories with 5,235 keywords. The analysis is validated to 99% scientific confidence (Fleiss' Kappa).
Can FRAMEGATE Predict Media Cycles?
FRAMEGATE makes patterns stable enough that forecasting becomes meaningful—identifying when calm-to-spike transitions are probable and which trigger classes drive them.
When Is FRAMEGATE Launching?
FRAMEGATE is launching in 2026. Early access and partnership inquiries are welcome.
FRAMEGATE doesn't help you react faster. It helps you see earlier. The platform is the product form of what narrative intelligence practically delivers: strategic sight instead of automated opinion.
Scientific Sources
I. Psychological Models & Motivation
- Schwartz, S. H. (1992). Universals in the content and structure of values. Advances in Experimental Social Psychology, 25, 1-65.
- Schwartz, S. H. (2012). An overview of the Schwartz theory of basic values. Online Readings in Psychology and Culture, 2(1).
- Haidt, J. (2012). The Righteous Mind: Why Good People Are Divided by Politics and Religion. Vintage Books.
- Graham, J., Haidt, J., & Nosek, B. A. (2009). Liberals and conservatives rely on different sets of moral foundations. Journal of Personality and Social Psychology, 96(5), 1029-1046.
- Deci, E. L., & Ryan, R. M. (1985). Intrinsic Motivation and Self-Determination in Human Behavior. Plenum Press.
- Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation. American Psychologist, 55(1), 68-78.
- Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52(12), 1280-1300.
- Higgins, E. T. (1998). Promotion and prevention: Regulatory focus as a motivational principle. Advances in Experimental Social Psychology, 30, 1-46.
- McClelland, D. C. (1975). Power: The Inner Experience. Irvington.
- Winter, D. G. (1973). The Power Motive. Free Press.
- Vroom, V. H. (1964). Work and Motivation. Wiley.
- Rogers, R. W. (1975). A protection motivation theory of fear appeals and attitude change. Journal of Psychology, 91(1), 93-114.
- Hobfoll, S. E. (1989). Conservation of resources: A new attempt at conceptualizing stress. American Psychologist, 44(3), 513-524.
- Costa, P. T., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO-PI-R). Psychological Assessment Resources.
- Goldberg, L. R. (1990). An alternative "description of personality": The Big-Five factor structure. Journal of Personality and Social Psychology, 59(6), 1216-1229.
- Elliot, A. J. (2006). The hierarchical model of approach-avoidance motivation. Motivation and Emotion, 30(2), 111-116.
- Porges, S. W. (2011). The Polyvagal Theory. W.W. Norton.
- Porges, S. W. (2007). The polyvagal perspective. Biological Psychology, 74(2), 116-143.
II. Framing & Political Communication
- Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51-58.
- Entman, R. M. (2004). Projections of Power: Framing News, Public Opinion, and U.S. Foreign Policy. University of Chicago Press.
- Lakoff, G. (2004). Don't Think of an Elephant! Know Your Values and Frame the Debate. Chelsea Green.
- Lakoff, G. (2002). Moral Politics: How Liberals and Conservatives Think. University of Chicago Press.
- Lakoff, G. (2008). The Political Mind. Penguin.
- Lakoff, G., & Johnson, M. (1980). Metaphors We Live By. University of Chicago Press.
- Semetko, H. A., & Valkenburg, P. M. (2000). Framing European politics. Journal of Communication, 50(2), 93-109.
- de Vreese, C. H. (2005). News framing: Theory and typology. Information Design Journal, 13(1), 51-62.
- McCombs, M. E., & Shaw, D. L. (1972). The agenda-setting function of mass media. Public Opinion Quarterly, 36(2), 176-187.
- McCombs, M. E. (2004). Setting the Agenda. Polity Press.
- Scheufele, D. A., & Tewksbury, D. (2007). Framing, agenda setting, and priming. Journal of Communication, 57(1), 9-20.
- Iyengar, S., & Kinder, D. R. (1987). News That Matters. University of Chicago Press.
III. Persuasion & Rhetoric
- Cialdini, R. B. (1984/2021). Influence: The Psychology of Persuasion. Harper Business.
- Cialdini, R. B. (2016). Pre-Suasion. Simon & Schuster.
- Aristotle. Rhetoric (ca. 350 BCE).
- Cicero. De Oratore (55 BCE).
- Quintilian. Institutio Oratoria (ca. 95 CE).
IV. Mass Psychology & Propaganda
- Le Bon, G. (1895). The Crowd: A Study of the Popular Mind. Dover.
- Le Bon, G. (1912). The Psychology of Revolution. G.P. Putnam's Sons.
- Bernays, E. L. (1928). Propaganda. Horace Liveright.
- Bernays, E. L. (1923). Crystallizing Public Opinion. Boni and Liveright.
- Herman, E. S., & Chomsky, N. (1988). Manufacturing Consent. Pantheon Books.
- Lippmann, W. (1922). Public Opinion. Harcourt, Brace.
- Lippmann, W. (1925). The Phantom Public. Harcourt, Brace.
V. Political Philosophy & Strategy
- Machiavelli, N. (1532). The Prince. Penguin Classics.
- Machiavelli, N. (1531). Discourses on Livy. University of Chicago Press.
- Luntz, F. (2007). Words That Work. Hyperion.
- Westen, D. (2007). The Political Brain. PublicAffairs.
VI. Narratology & Storytelling
- Propp, V. (1928/1968). Morphology of the Folktale. University of Texas Press.
- Campbell, J. (1949). The Hero with a Thousand Faces. New World Library.
- Vogler, C. (1992). The Writer's Journey. Michael Wiese Productions.
- Greimas, A. J. (1966). Structural Semantics. University of Nebraska Press.
VII. Cognitive Psychology
- Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
- Kahneman, D., & Tversky, A. (1979). Prospect theory. Econometrica, 47(2), 263-291.
- Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.
- Gigerenzer, G. (2007). Gut Feelings. Viking.
- Ariely, D. (2008). Predictably Irrational. Harper.
- Thaler, R. H., & Sunstein, C. R. (2008). Nudge. Yale University Press.
VIII. Buddhist Phenomenology
- Bodhi, B. (2000). The Connected Discourses of the Buddha (Samyutta Nikaya). Wisdom Publications.
- Analayo, B. (2003). Satipatthana: The Direct Path to Realization. Windhorse.
- Gethin, R. (1998). The Foundations of Buddhism. Oxford University Press.
IX. AI & Machine Learning
- Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018). Foundations of Machine Learning. MIT Press.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
- Prince, S. J. D. (2023). Understanding Deep Learning. MIT Press.
- Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
- Bellemare, M. G., Dabney, W., & Rowland, M. (2023). Distributional Reinforcement Learning. MIT Press.
- Murphy, K. P. (2022). Probabilistic Machine Learning: An Introduction. MIT Press.
- Barocas, S., Hardt, M., & Narayanan, A. (2023). Fairness and Machine Learning.
X. Methodology
- Krippendorff, K. (2018). Content Analysis. Sage.
- Neuendorf, K. A. (2017). The Content Analysis Guidebook. Sage.
- Fleiss, J. L. (1971). Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), 378-382.
- Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing.
XI. Additional Classics
- Goffman, E. (1974). Frame Analysis. Harvard University Press.
- Zaller, J. R. (1992). The Nature and Origins of Mass Opinion. Cambridge University Press.
- Gamson, W. A. (1992). Talking Politics. Cambridge University Press.
- Gitlin, T. (1980). The Whole World Is Watching. UC Berkeley Press.
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