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koriweb d8a80f6272 chore(wiki): dangling 링크 canonical 정규화 (768파일/1200건)

이름만 다른(표기 변형) [[위키링크]]를 대상 문서의 canonical 제목으로 치환해
끊겼던 1,200개 링크를 연결. 제목/파일명 정규화 일치만 적용하고 별칭 매칭은
과병합 위험으로 제외(애매성 가드). 원본은 _link_reconcile_backup/ 에 백업.
도구: Datacollect/scripts/link_reconcile_apply.mjs

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

2026-06-08 12:24:15 +09:00

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title

Ethnographic Research

매 한 줄

"매 people-in-context 의 deep, in-situ, often-long observational study". Malinowski (Trobriand 1922), Geertz "thick description" (1973) → 매 industry: Xerox PARC (Suchman 1980s) → 매 modern UX/HCI/Product 의 staple. 매 "what people say vs what people do" 의 gap 의 reveal 의 가장 강력한 method.

매 핵심

매 vs neighbors

Survey/usability test: 매 controlled / artificial / "say".
Interview: 매 retrospective / "say".
Ethnography: 매 in-situ / longitudinal / "do" + meaning.
Contextual Inquiry (Beyer & Holtzblatt 1998): 매 industry-condensed ethnography (1–2 hr in real workplace).
Diary study: 매 self-report longitudinal.
Auto-ethnography: 매 researcher = subject.

매 process (Spradley DRS / 12-step adapted)

Locate setting (gatekeeper, access, ethics/IRB).
Participant observation (4 modes: complete observer → complete participant).
Field notes (jottings → expanded → analytic memos).
Domain analysis (cultural categories).
Taxonomic analysis (relations within domain).
Componential analysis (attributes / contrasts).
Theme synthesis (cross-domain patterns).
Member checks (validate with participants).
Thick description write-up.

매 typical artifacts

Field notes (jotted + expanded), photo / video / audio (with consent), artifacts collected, journey maps, persona-from-data, JTBD jobs.

💻 패턴

Field-note template (Markdown)

# Field Note — 2026-05-10 — site:Hospital ER, observer:RP
## Setting
- 14:00–17:00, Triage desk, 3 nurses, ~40 patients.
## Activities (chronological)
- 14:03 nurse A swivels between EHR (slow) + paper backup …
## Verbatim quotes
- "I never trust the system after a shift change." — Nurse A, 14:22
## Surprises / breakdowns
- EHR auto-logout at 5 min idle → workaround = mouse jiggler.
## Analytic memo
- Domain: trust in tools. Hypothesis: short timeout drives shadow IT.
## Next steps
- Interview Nurse B; check audit logs for jiggler signatures.

Coding qualitative data (open + axial, in Python)

import pandas as pd
notes = pd.read_csv("interviews.csv")  # cols: pid, turn, text
codes = {
    "trust-tool":   ["never trust", "doesn't work", "I just write it down"],
    "workaround":   ["mouse jiggler", "shared password", "screenshot"],
    "time-pressure":["no time", "rushing", "back-to-back"],
}
def code(t):
    return [c for c, kws in codes.items() if any(k in t.lower() for k in kws)]
notes["codes"] = notes.text.apply(code)
notes.explode("codes").groupby("codes").size().sort_values(ascending=False)

Affinity diagram digitization (Miro-style → DataFrame)

import pandas as pd
stickies = pd.DataFrame({
    "note": ["EHR logout 5 min", "Paper backup chart", "Phone snapshots", ...],
    "cluster": ["timeouts", "shadow records", "shadow records", ...]
})
clusters = stickies.groupby("cluster")["note"].apply(list)

Journey-map dataclass

from dataclasses import dataclass
from typing import List
@dataclass
class Step:
    actor: str; action: str; tool: str; emotion: str; pain: str
journey: List[Step] = [
    Step("nurse", "log in", "EHR", "neutral",  "5-min timeout"),
    Step("nurse", "triage", "paper+EHR", "stress", "duplicate entry"),
]

LLM-assisted thematic analysis (with caching)

import anthropic
client = anthropic.Anthropic()
def themes(transcript: str) -> str:
    return client.messages.create(
        model="claude-opus-4-7",
        max_tokens=1500,
        system=[{"type":"text","text":"You are a senior qualitative researcher."
                ,"cache_control":{"type":"ephemeral"}}],
        messages=[{"role":"user","content":
            f"Identify 3-7 emergent themes (open-coding style) with quote evidence.\n\n{transcript}"}]
    ).content[0].text

import re
def redact_pii(s: str) -> str:
    s = re.sub(r"\b\d{3}-\d{2}-\d{4}\b", "[SSN]", s)
    s = re.sub(r"\b[\w.+-]+@[\w-]+\.[\w.-]+\b", "[EMAIL]", s)
    return s

매 결정 기준

상황	Approach
Need rich context, hidden practice	Full ethnography (weeks–months)
Industry, tight timeline	Rapid / focused ethnography (days)
Workplace tool design	Contextual Inquiry
Distributed / remote users	Diary study + remote shadowing
Sensitive populations	Auto-ethnography or co-design
Quantify after	Mixed methods: ethnography → survey → A/B

기본값: 매 product discovery 의 5-7 contextual inquiries (90 min each) + open coding + affinity diagram.

🔗 Graph

부모: HCI
변형: Contextual-Inquiry · Autoethnography
Adjacent: Grounded-Theory

🤖 LLM 활용

언제: 매 transcript 의 first-pass open coding, 매 affinity cluster 의 candidate, 매 quote retrieval, 매 persona drafting. 언제 X: 매 final theme 의 sole arbiter (매 researcher judgment 필수), 매 sensitive raw data 의 unconsented external API call.

❌ 안티패턴

"Asking" 만 하기: 매 ethnography 의 essence = observing, not interviewing alone.
One-shot 1-hour visit + claim "ethnography": 매 contextual inquiry 라고 부르는 의 정직.
No reflexivity: 매 observer effect / bias 의 acknowledged 없으면 매 weak.
Confirmation bias coding: 매 second coder + inter-rater reliability (Cohen's κ) 의 add.
Thin description: 매 "users were frustrated" — 매 thick description 의 absent (no actor, action, meaning).
Skip consent / IRB: 매 ethical 의 mandatory.

🧪 검증 / 중복

Verified (Malinowski 1922; Geertz 1973; Spradley 1979/1980; Beyer & Holtzblatt Contextual Design 1998; Kuniavsky Observing the User Experience 2nd ed.).
신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1 placeholder
2026-05-10	Manual cleanup — Spradley process + 6 patterns + LLM coding

6.4 KiB Raw Blame History Unescape Escape