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Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
基于 SOC 职业分类
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| name | grounded-theory-guide |
| description | Apply grounded theory methodology to develop theory from data |
| metadata | {"openclaw":{"emoji":"🌱","category":"research","subcategory":"methodology","keywords":["grounded theory","qualitative methodology","theoretical sampling","constant comparison","coding"],"source":"wentor-research-plugins"}} |
A skill for applying grounded theory methodology (GTM) to generate theory grounded in empirical data. Covers the three major schools (Glaser, Strauss/Corbin, Charmaz), coding procedures, theoretical sampling, memo writing, and criteria for evaluating grounded theories.
| Aspect | Classic (Glaser) | Straussian (Strauss & Corbin) | Constructivist (Charmaz) |
|---|---|---|---|
| Ontology | Objective reality | Pragmatist | Relativist/constructivist |
| Literature review | Delay until theory emerges | Early but non-constraining | Early, reflexive engagement |
| Coding paradigm | Open, selective, theoretical | Open, axial, selective | Initial, focused, theoretical |
| Verification | Emergent fit | Systematic validation | Co-construction with participants |
| Core output | Substantive theory | Process model | Interpretive theory |
| Key text | Glaser (1978) | Strauss & Corbin (1998) | Charmaz (2014) |
Use Classic GTM when:
- You want the theory to emerge with minimal preconception
- You are studying a process in a substantive area
Use Straussian GTM when:
- You need a structured, systematic coding procedure
- Your discipline values replicable analytical steps
Use Constructivist GTM when:
- You acknowledge the researcher's role in co-creating meaning
- You study experiences, identities, or social processes
- You work in health, education, or social science
def grounded_theory_coding_stages() -> dict:
"""
Describe the three stages of grounded theory coding.
"""
return {
"stage_1_initial_coding": {
"also_called": "Open coding",
"description": (
"Examine data line by line or incident by incident. "
"Generate codes that stay close to the data. "
"Use gerunds (action words ending in -ing) to capture processes."
),
"example": {
"data": "I started looking for help online because the doctor "
"did not explain anything to me.",
"codes": [
"Seeking information online",
"Experiencing communication gap with provider",
"Taking initiative in own care"
]
},
"tips": [
"Code quickly -- do not overthink individual codes",
"Stay open; do not force data into preexisting categories",
"Code actions and processes, not topics",
"Write memos about ideas that arise during coding"
]
},
"stage_2_focused_coding": {
"also_called": "Axial coding (Strauss) or Focused coding (Charmaz)",
"description": (
"Select the most frequent and significant initial codes. "
"Use them to sort and synthesize larger amounts of data. "
"Identify relationships between categories."
),
"tasks": [
,
,
,
]
},
: {
: ,
: (
),
:
}
}
Traditional sampling: Decide sample before data collection
Theoretical sampling: Let the emerging theory guide who/what to sample next
Process:
1. Collect initial data (purposive sampling)
2. Analyze data, identify emerging categories
3. Ask: "Where should I look next to develop these categories?"
4. Sample deliberately to fill gaps in the emerging theory
5. Continue until theoretical saturation
Example:
Initial interviews: Patients with chronic illness
Emerging category: "Navigating insurance barriers"
Next sample: Interview insurance navigators and social workers
Emerging category: "Stigma in seeking help"
Next sample: Interview patients who avoided seeking help
Memos are the researcher's running commentary on codes, categories, and theoretical ideas. They are the primary mechanism for developing theory.
Memo types:
- Code memos: Define and elaborate a code or category
- Theoretical memos: Explore relationships between categories
- Operational memos: Record methodological decisions
- Reflexive memos: Examine researcher influence on the analysis
Memo example:
MEMO: "Becoming an expert patient" (2026-03-05)
Several participants describe a transition from passive
recipient of care to active manager of their condition.
This process seems to involve three phases: (1) initial
confusion and dependence, (2) information seeking and
experimentation, (3) confident self-management. The trigger
appears to be a critical incident (a misdiagnosis, a bad
interaction with a provider) that motivates the person to
take control. Compare with Corbin & Strauss's trajectory
framework. Need to sample someone early in the trajectory
to test whether the trigger is consistent.
| Criterion | Description | How to Demonstrate |
|---|---|---|
| Fit | Theory fits the data it was derived from | Show clear evidence trail from data to codes to categories |
| Relevance | Theory addresses a real concern of participants | Member checking, resonance with practitioners |
| Workability | Theory explains the process and enables prediction | Apply the theory to new cases |
| Modifiability | Theory can be updated with new data | Show how the theory evolved during the study |
| Credibility | Analysis is thorough and systematic | Audit trail, reflexive memos, theoretical saturation |
Include: a clear description of the coding process and how categories were derived, a diagram or model of the theory, representative quotes for each major category, an explanation of theoretical sampling decisions, and a discussion of how the theory relates to existing literature. Use the SRQR (Standards for Reporting Qualitative Research) checklist to ensure completeness.