Source Credibility Matrix

Template Overview

In an era of algorithmic search manipulation, sponsored influencer reviews, and synthetic content, treating all web sources equally leads to intellectual decay. The Source Credibility Matrix applies rigorous analytical skepticism to any book, report, whitepaper, or article before its assertions are admitted into your permanent knowledge vault. Utilizing the CRAAP framework and epistemic incentives analysis, it guards the intellectual hygiene of your second brain.

Markdown Template Structure

Copy the block below or click Download .md File to add this template directly to your personal vault:

---
title: "Credibility Audit: [Source Title / Publisher]"
date_evaluated: [YYYY-MM-DD]
source_author_or_org: "[[Author / Organization]]"
primary_domain: "[Machine Learning / Cybersecurity / Macroeconomics / Medicine]"
craap_composite_score: [Score / 25]
epistemic_tier: "[Tier 1 Gold Standard / Tier 2 Credible with Caveats / Tier 3 Untrusted]"
tags: [critical-thinking, source-evaluation, craap-test, epistemology, pkm, fact-checking]
---

# Source Credibility Matrix — [Source Title]

## 1. Bibliographic Identity & Epistemic Origin
- **Source Full Title:** `[Full Document Title]`
- **Author / Publishing Entity:** `[Author Name, Institutional Affiliation]`
- **Funding & Sponsorship Disclosures:** Who paid for the research or publication?
- **Core Claim Being Evaluated:** "On-device AI cannot achieve usable accuracy without cloud offloading."

## 2. The CRAAP Framework Evaluation
*Score each dimension from 1 (Fails completely) to 5 (Exemplary rigor):*
| Evaluation Dimension | Score (1-5) | Specific Evaluator Observations & Justifications |
|---|---|---|
| **Currency (Timeliness)** | 2 / 5 | Published in 2021 before the rollout of Apple Silicon M-series neural chips; hopelessly obsolete regarding edge memory bandwidth. |
| **Relevance (Fit)** | 4 / 5 | Directly addresses consumer speech transcription architectures. |
| **Authority (Credibility)** | 4 / 5 | Lead author is a respected university professor with 40+ cited publications. |
| **Accuracy (Verifiability)** | 2 / 5 | Benchmarks were run on simulated server hardware rather than physical laptop devices; claims not reproducible. |
| **Purpose (Objectivity)** | 1 / 5 | Study was co-sponsored by a major cloud hyperscaler whose business model relies exclusively on cloud API billing. |
| **Total Composite Score** | **13 / 25** | **Result: Epistemic Tier 3 (Untrusted / Heavily Biased)** |

## 3. Conflict of Interest & Incentive Analysis
- **The Financial Incentive:** The publishing foundation is funded by cloud server providers. Concluding that local laptops can replace cloud servers would directly contradict the donor's commercial interests.
- **The Prestige Incentive:** Authors have vested interests in preserving traditional cloud machine learning paradigms they spent a decade teaching.

## 4. Empirical Falsification & Reality Check
- Our internal physical benchmarks on M3 hardware (`[[computational-lab-notebook]]`) directly contradict the author's theoretical latency claims by a factor of 4x.

## 5. Final Vault Admission Verdict
- **Verdict:** **Reject as Authoritative Fact; Retain as Case Study in Industry Bias.**
- **Action Taken:** Do NOT link this paper's claims as truth. File under `[[case-study-narrative-arc]]` illustrating cloud vendor marketing narratives.

Filled Real-World Example

Here is a realistic example demonstrating how this template captures actual deliberations:

---
title: "Credibility Audit: Cloud AI Whitepaper 2026"
date_evaluated: 2026-10-09
source_author_or_org: "Hyperscale Cloud Institute"
primary_domain: "Cloud Computing"
craap_composite_score: 11
epistemic_tier: "Tier 3 Untrusted"
tags: [critical-thinking, source-evaluation, craap-test, epistemology, pkm, fact-checking]
---

# Source Credibility Matrix — Cloud AI Whitepaper 2026

## 1. Summary
The whitepaper claims that on-device AI transcription is 'too computationally intensive for consumer laptops'.

## 2. Finding
Whitepaper was funded directly by a cloud API vendor charging $0.01 per audio minute. Evaluated on outdated 2020 hardware. Fails CRAAP test on Currency, Accuracy, and Purpose. Retained only as an example of vendor bias.

When to Use and When to Avoid

When to Use

• Use when evaluating controversial industry whitepapers, sponsored studies, or viral technical claims.
• Use before citing external statistics or scientific claims in your own published books or papers.
• Use to preserve intellectual hygiene and prevent marketing propaganda from corrupting your vault.

When to Avoid

• Avoid using for verified, peer-reviewed textbook mathematics or settled historical facts.
• Avoid using for personal creative writing.

On-Device AI Integration

Summarize your recording in Oyma with this local instruction:

Analyze the source credibility assessment:
1. Cross-examine the CRAAP scores against the described funding disclosures and date of publication.
2. Identify hidden cognitive biases or financial conflicts that may have influenced the source's methodology.
3. Formulate a 1-paragraph critical appraisal summarizing why this source must be cited with caveats.
Output in structured Markdown.

Your data remains securely on your Mac with zero cloud exposure.