# Research Methodology and Quality Standards

This document describes the methodology and quality standards applied to the research papers in this collection.

## Collection Overview

This collection contains 12 synthetic research papers examining local-first AI deployment in regulated environments. All papers are original, created specifically for this research synthesis exercise.

## Paper Selection Criteria

### Inclusion Criteria

Papers were selected based on:

1. **Relevance**: Directly addresses local-first AI, privacy, security, or governance
2. **Diversity**: Represents multiple perspectives and methodologies
3. **Conflict Intentionality**: Includes at least one point of disagreement with other papers
4. **Quality Standards**: Meets minimum research rigor standards
5. **Recency**: Reflects current (2024) state of knowledge

### Diversity Dimensions

Papers were selected to represent:

| Dimension         | Range Represented                             |
| ----------------- | --------------------------------------------- |
| Methodology       | Quantitative, qualitative, mixed              |
| Perspective       | Pro-local, pro-cloud, balanced, critical      |
| Sector            | Healthcare, finance, technology, cross-sector |
| Geography         | US, EU, global perspectives                   |
| Organization Size | Startup, SME, enterprise                      |

## Quality Standards

### Minimum Rigor Requirements

All papers meet these standards:

1. **Clear methodology description**: Sufficient detail for replication
2. **Data transparency**: Sources and collection methods documented
3. **Limitations acknowledged**: Weaknesses explicitly stated
4. **Evidence-based claims**: Assertions supported by data or analysis
5. **Balanced presentation**: Acknowledgment of alternative viewpoints

### Conflict Documentation Standards

When papers disagree, the conflict is documented with:

1. **Specific claims**: Exact wording of opposing positions
2. **Evidence basis**: What evidence supports each position
3. **Assessment criteria**: How to evaluate conflicting claims
4. **Context factors**: What might explain the disagreement

## Known Conflicts in Collection

This collection intentionally includes papers that disagree on key findings:

| Topic                     | Paper A Position                | Paper B Position                   |
| ------------------------- | ------------------------------- | ---------------------------------- |
| Security (local vs cloud) | Paper 02: Local more secure     | Paper 08: Cloud often more secure  |
| Cost effectiveness        | Paper 04: Local competitive     | Paper 09: Cloud 40-60% cheaper     |
| Implementation success    | Paper 06: Hybrid optimal        | Paper 07: Pure approaches better   |
| Regulatory clarity        | Paper 05: Requirements clear    | Paper 10: Complete uncertainty     |
| Performance               | Paper 04: Local can match cloud | Paper 02: Cloud faster in practice |

## Data Sources and Attribution

### Primary Data Sources

| Source Type            | Papers Using          |
| ---------------------- | --------------------- |
| Organizational surveys | Papers 01, 04, 06, 07 |
| Case studies           | Papers 02, 05, 08, 10 |
| Expert interviews      | Papers 03, 09         |
| Technical analysis     | Papers 04, 11, 12     |

### Synthetic Nature Note

All data in these papers is synthetic—generated for educational and analytical purposes. No real organizations, proprietary data, or confidential information is included.

## Terminology Standards

### Consistent Terms

All papers use consistent definitions for key terms:

| Term           | Definition                                     |
| -------------- | ---------------------------------------------- |
| Local-First AI | AI systems running primarily on local hardware |
| Cloud AI       | AI services accessed via remote APIs           |
| Hybrid AI      | Combined local and cloud approach              |
| Privacy-First  | Design philosophy prioritizing data privacy    |

### Varied Usage

Some terms are used differently across papers, reflecting real-world variation:

| Term          | Usage Variation                                                  |
| ------------- | ---------------------------------------------------------------- |
| Edge AI       | Sometimes synonymous with local-first, sometimes specific to IoT |
| On-Premise    | May or may not include edge devices                              |
| Private Cloud | Interpretation varies significantly                              |

## Limitations of This Collection

1. **Synthetic data**: Not based on real research
2. **Publication bias**: Papers tend toward positive findings
3. **Limited scope**: Focus on specific use cases and contexts
4. **Temporal snapshot**: Reflects 2024 knowledge, will age
5. **No peer review**: Not vetted by independent experts

## How to Use This Collection

### For Synthesis Exercises

1. Start with workspace_scan to understand all papers
2. Use questions.md to guide analysis
3. Use glossary.md for consistent terminology
4. Document conflicts as you identify them
5. Create your own synthesis based on evidence

### For Teaching

1. Use papers to demonstrate different methodologies
2. Compare how different authors approach same topics
3. Identify strengths and weaknesses in arguments
4. Practice conflict resolution across sources
5. Discuss the synthetic nature and its implications

### For Research

1. Use as a template for real literature reviews
2. Practice systematic review methods
3. Learn to identify conflict and consensus
4. Develop synthesis skills
5. Understand research limitations

## Quality Scoring

Each paper includes a quality assessment in its appendix:

| Criterion   | Weight | Description                       |
| ----------- | ------ | --------------------------------- |
| Methodology | 25%    | Rigor and appropriateness         |
| Evidence    | 25%    | Quality and quantity of support   |
| Analysis    | 20%    | Depth and validity of conclusions |
| Clarity     | 15%    | Writing and organization          |
| Limitations | 15%    | Acknowledgment of weaknesses      |

## Updates and Revisions

This is version 1.0 of the collection (January 2026).

Potential future updates may include:

- Additional papers on emerging topics
- Updated versions reflecting new research
- Corrections if errors are identified
- Expanded scope if needed
