ResearchMethod
This is not "deep research by prompt."
This workflow was designed for human-led research with AI as a collaborator — not an authority, not a conclusion machine, and not a shortcut past hard thinking.
AI is used here to map intellectual terrain quickly, surface contradictions, organize competing interpretations, and suggest where deeper study is needed. AI is not used to decide what is true. Some outputs may be incomplete or wrong. That is expected.
The purpose of this method is to compress time without compressing understanding — by forcing disagreement, structure, and coherence to surface early. It is doing the research yourself, faster and more honestly, with AI helping at the edges.
"Truth is rarely found where everyone agrees. It is found where disagreement is sharpest and still survivable."
Why This Question Matters — Personal Callout
Why This Question Matters
I believe very strongly that we are heading toward economic, social, and financial collapse. The research question isn't academic to me. It comes from somewhere more personal than that.
I feel the weight of this in a way I can't fully explain — like there's something I'm supposed to do about it even though I recognize how delusional that sounds. That's where I'm at in life. I feel like I should do something about it.
This is not a commitment to having the answers. It is a starting point for asking the right questions.
Personal Note — David Lowe
The research question driving this project: Why is there a ubiquitous, measurable change in moral decline in America from the early 1900s to present day?
Before answering a question, ask what kind of question it is. This one spans sociology, theology, economics, family structure, institutional trust, and philosophy. It cannot be answered by a single discipline. It requires a method that can hold that complexity without collapsing it.
The Five-Page System
The Five-Page System
Each inquiry in this project uses five pages. They are not optional and they are not decorative. Each page performs a distinct cognitive function. Duplicate all five for each new question.
Inquiry Seed
Question & Orientation
States the research question precisely. Captures the researcher's initial intuition or belief before any AI input contaminates it. This matters: your pre-AI thinking is load-bearing. If you let AI frame the question, you've already lost authorship of the answer.
File: Inquiry_Seed_[Question].md
AI Prompt Engine
How AI Is Used
Documents the exact prompts used, the AI models consulted, and the outputs received — without yet evaluating them. The AI output is pasted or summarized here raw. No judgment at this stage. The engine maps the terrain: competing interpretations, key names, major fault lines, suggested search domains. It does not decide what is true.
File: AI_Prompt_Engine_[Question].md
Research Workspace
Evidence & Links
The living document where actual evidence accumulates. Primary sources, data links, study citations, statistics with dates and sourcing. This is where AI suggestions get stress-tested against real sources. Not every AI claim survives contact with the primary literature. The workspace is where that collision happens.
File: Research_Workspace_[Question].md
Contradictions & Resolution
Sense-Making
This is the most important page in the system. Where do the sources disagree? Where does the AI output contradict the primary literature? Where does your intuition conflict with the data? This page forces you to sit with irresolution long enough to understand it — rather than selecting only evidence that confirms the hypothesis you arrived with.
Truth is most often found in the contradiction that refuses to resolve cleanly, not in the consensus that resolves too easily.
File: Contradictions_Resolution_[Question].md
Quotes, Domains, & Links
Shared Library
A shared reference library built across all inquiries. Key quotes with attribution and date. Domain categories surfaced during research. External links with brief context annotations. This page is cumulative — it grows across every inquiry and builds the raw material from which publications are drawn.
File: Quotes_Domains_Links_[Question].md
What AI Does and Does Not Do
What AI Does — and Does Not Do
AI Is Used To
- Map intellectual terrain quickly across multiple disciplines
- Surface contradictions between competing interpretations
- Organize competing frameworks side by side
- Suggest where deeper primary-source study is needed
- Stress-test early conclusions against counterarguments
AI Is Not Used To
- Decide what is true
- Replace primary-source verification
- Frame the initial question before the researcher does
- Select which evidence counts as significant
- Resolve contradictions that require human judgment
Some AI outputs in this project are incomplete. Some are wrong. That is expected and is not a failure of the method. The method treats AI error as a signal — it tells you where the intellectual terrain is genuinely contested, where consensus has been manufactured rather than earned, and where you need to dig deeper into primary sources.
How to Use This System
How to Use This System
Workflow Sequence
Start with the Inquiry Seed. Write your question and your initial intuition before running any AI prompt.
Run the AI Prompt Engine. Paste the output. Do not judge it yet — just collect it.
Open the Research Workspace. Follow the AI's leads into primary sources. Cite everything. Record what the data actually says.
Build the Contradictions page. Force the disagreements into the open. Sit with them. Do not paper over them.
Maintain the Quotes, Domains, & Links library as you go. Every session adds to it.
Duplicate all five pages for each new question. The structure is not bureaucracy — it is the cognitive architecture that prevents AI-assisted research from collapsing into AI-generated opinion.
Closing
"Truth is rarely found where everyone agrees. It is found where disagreement is sharpest and still survivable."
This research exists because someone felt the weight of it and decided that feeling was not enough. You have to actually look. You have to go where the data is uncomfortable. You have to let the contradictions speak before you reach for resolution.
That is what this project is. That is what this method is for.