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