2nd/10_Wiki/Topic_General/From_Other/Victimhood-Narratives.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-victimhood-narratives	Victimhood Narratives	10_Wiki/Topics	verified	self	Victim Narrative Tendency for Interpersonal Victimhood TIV	none	A	0.85	applied	psychology sociology narrative ethics		2026-05-10	pending	language framework english-korean social-psychology

Victimhood Narratives

매 한 줄

"매 personal/group identity 의 wronged-self 의 frame". Gabay et al. (2020) 의 Tendency for Interpersonal Victimhood (TIV) 의 4-factor scale 의 academic 의 codify. 매 narrative 의 mobilizing power 의 strong — collective grievance, political identity, online discourse 의 central. 매 legitimate harm 의 acknowledge 의 vs 매 strategic identity 의 instrumentalize 의 distinction 의 critical.

매 핵심

매 TIV Four Factors (Gabay 2020)

1. Need for recognition — 매 victim status 의 external validate.
2. Moral elitism — 매 self / in-group 의 moral 의 superior 의 see.
3. Lack of empathy — 매 own pain focus, others' 의 dismiss.
4. Rumination — 매 past offense 의 repeated 의 replay.

매 Functions

• Solidarity — 매 in-group 의 cohesion 의 strengthen.
• Mobilization — 매 collective action 의 fuel.
• Moral leverage — 매 demand 의 legitimacy 의 add.
• Avoidance — 매 personal agency 의 displace 의 onto external.

매 Risks

• Competitive victimhood — 매 group 의 grievance Olympics.
• Identity rigidity — 매 victim 의 permanent 의 self-cast.
• Discourse polarization — 매 zero-sum 의 frame.
• Manipulation — 매 demagogue 의 exploit.

매 응용

1. Social psych research — TIV scale 의 measure.
2. Conflict mediation — 매 dual-narrative recognition 의 break impasse.
3. Political analysis — 매 movement rhetoric 의 deconstruct.
4. Therapy — 매 individual 의 reframing 의 agency 의 reclaim.

💻 패턴

TIV scale scoring (Python)

# Gabay et al. 2020 — 8 items per factor, 5-point Likert
def tiv_score(responses: dict[str, list[int]]) -> dict[str, float]:
    factors = ["need_recognition", "moral_elitism", "empathy_lack", "rumination"]
    return {f: sum(responses[f]) / len(responses[f]) for f in factors}

scores = tiv_score({
    "need_recognition": [4, 5, 3, 5, 4, 4, 3, 5],
    "moral_elitism":    [3, 4, 4, 3, 4, 3, 4, 4],
    "empathy_lack":     [2, 3, 2, 3, 2, 2, 3, 2],
    "rumination":       [5, 5, 4, 5, 5, 4, 5, 5],
})
print(scores)  # {'need_recognition': 4.13, ...}

Narrative frame classifier (LLM)

import anthropic
client = anthropic.Anthropic()

def classify_frame(text: str) -> str:
    resp = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=200,
        messages=[{
            "role": "user",
            "content": f"""Classify the narrative frame of this passage as one of:
- legitimate_grievance (specific, verifiable harm + agency)
- victimhood_identity (TIV-style: rumination, moral elitism, no agency)
- mixed
- neither

Passage: {text}

Return JSON: {{"frame": "...", "rationale": "..."}}"""
        }]
    )
    return resp.content[0].text

Dual-narrative mediation template

**Both/And reframe**

Group A's harm: <specific historical/ongoing>.
Group B's harm: <specific historical/ongoing>.

Shared interest: <identified common ground>.

Action item:
- A acknowledges B's <specific>.
- B acknowledges A's <specific>.
- Joint commitment: <forward action>.

Discourse rumination detector (NLP)

import re
def rumination_index(text: str) -> float:
    # Crude: repetition of grievance markers
    markers = re.findall(r"\b(again|always|still|never|every time|once more)\b",
                         text, re.I)
    sentences = re.split(r"[.!?]", text)
    return len(markers) / max(len(sentences), 1)

Survey deployment (Qualtrics-style YAML)

survey: TIV-2020-short
items:
  - id: tiv_nr_1
    text: "It is important to me that people who have hurt me acknowledge my pain."
    scale: likert-5
  - id: tiv_me_1
    text: "I have a higher moral standard than most people."
    scale: likert-5
# ... 32 items total

매 결정 기준

상황	Frame
Verifiable specific harm + agency call	Legitimate grievance
Diffuse identity claim, rumination, no agency	TIV-style
Power asymmetry context	매 careful — 매 dismissal 의 risk
Therapy 1:1	Reframe 의 agency 의 restore
Public discourse	Acknowledge harm + reject zero-sum

기본값: 매 specific harm 의 acknowledge AND 매 identity-rigidity 의 caution.

🔗 Graph

🤖 LLM 활용

언제: narrative frame 의 analyze, dual-acknowledgment 의 draft, TIV survey 의 design. 언제 X: 매 individual 의 lived experience 의 dismiss — 매 specific harm 의 verify, 매 LLM 의 not arbiter.

❌ 안티패턴

• Blanket dismissal: 매 "victim mentality" label 의 specific harm 의 erase.
• Blanket validation: 매 every claim 의 unconditional accept — 매 manipulation vector.
• Zero-sum framing: 매 only one group 의 victim 의 — 매 dual-narrative 의 ignore.
• Therapy 의 weaponize: 매 TIV scale 의 ad hominem 의 use.
• Historical denial: 매 documented systemic harm 의 "narrative" 의 reduce.

🧪 검증 / 중복

• Verified (Gabay et al. 2020, Personality and Individual Differences 165:110134).
• 신뢰도 A (academic) / B (politicized application — context-dependent).

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — TIV factors + classifier patterns + mediation template