README.md

---
license: mit
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- llama
- lora
- political-science
- survey-replication
- canadian-election-study
- peft
- unsloth
datasets:
- custom
language:
- en
pipeline_tag: text-generation
---

# CES Phase 3A LoRA: Leader Affect + Policy Positions

A LoRA adapter for Llama 3.1 8B Instruct that predicts political ideology from demographics, leader thermometer ratings, and wedge issue positions. This is the **recommended** model in the Phase 3 series.

## Model Description

This model was trained on the Canadian Election Study (CES) 2021 to predict self-reported ideology (0-10 left-right scale) from:

- **Demographics**: Age, gender, province, education, employment, religion, marital status, urban/rural, born in Canada
- **Leader Thermometers**: Ratings (0-100) of Justin Trudeau, Erin O'Toole, and Jagmeet Singh
- **Wedge Issues**: Positions on carbon tax, energy/pipelines, and medical assistance in dying (MAID)
- **Government Satisfaction**: Overall satisfaction with federal government

## Performance

| Model | Inputs | Correlation (r) |
|-------|--------|-----------------|
| Base Llama 8B | Demographics only | 0.03 |
| GPT-4o-mini | Demographics only | 0.285 |
| Phase 1 | Demographics only | 0.213 |
| Phase 2 | + Gov satisfaction, economy, immigration | 0.428 |
| **Phase 3A (this model)** | **+ Leader thermometers + wedge issues** | **0.560** |
| Phase 3B | + Party ID | 0.574 |

## Key Finding: "The Null Result of the Label"

We trained two versions of Phase 3:
- **Phase 3A** (this model): Uses leader ratings and policy positions, but NOT party identification
- **Phase 3B**: Adds party identification ("I usually think of myself as a Liberal/Conservative...")

**Result**: Adding party ID only improves correlation by 0.014 (from 0.560 to 0.574).

**What this means:**
- Party identity is **redundant** — it's already encoded in how people feel about leaders and their policy positions
- Canadian ideology is **substantive, not tribal** — people's "team" reflects their actual views
- **Phase 3A is preferred** — predicts ideology without "cheating" by asking party affiliation

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Meta-Llama-3.1-8B-Instruct",
    load_in_4bit=True
)
model = PeftModel.from_pretrained(base_model, "baglecake/ces-phase3a-lora")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct")

# Example prompt
system = """You are a 45-year-old man from Ontario, Canada. You live in a suburb of a large city. Your highest level of education is a bachelor's degree. You are currently employed full-time. You are married. You have children. You are Catholic. You were born in Canada.

Political Profile:
Leader Ratings: Justin Trudeau: 25/100, Erin O'Toole: 70/100, Jagmeet Singh: 30/100.
Views: Strongly disagrees that the federal government should continue the carbon tax; strongly agrees that the government should do more to help the energy sector/pipelines.
Overall Satisfaction: Is not at all satisfied with the federal government.

Answer survey questions as this person would, based on their background and detailed political profile."""

user = "On a scale from 0 to 10, where 0 means left/liberal and 10 means right/conservative, where would you place yourself politically? Just give the number."

# Format as Llama chat and generate
```

## Steerability

The model is steerable — changing leader ratings and policy positions shifts predicted ideology:

| Profile | Trudeau | O'Toole | Carbon Tax | Predicted |
|---------|---------|---------|------------|-----------|
| Liberal | 85/100 | 15/100 | Strongly agree | 3 (left) |
| Conservative | 10/100 | 90/100 | Strongly disagree | 8 (right) |
| Moderate | 50/100 | 55/100 | Neutral | 6 (center) |

**5-point ideology swing** from profile changes alone, holding demographics constant.

## Training Details

- **Base model**: meta-llama/Meta-Llama-3.1-8B-Instruct (4-bit quantized via Unsloth)
- **Training data**: 14,452 examples from CES 2021
- **LoRA rank**: 32
- **LoRA alpha**: 64
- **Target modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Epochs**: 3
- **Hardware**: NVIDIA A100 40GB (Colab Pro)

## Implications

This model is ideal for:
- Simulating political discourse with leader-specific affect
- Agent-based models where leader ratings drive polarization
- Studying how policy positions (not just party labels) shape ideology

Not suitable for:
- General political conversation (model only outputs 0-10 numbers)
- Elections with different leaders (trained on 2021 Trudeau/O'Toole/Singh)
- Predicting specific budget or policy preferences

## Limitations

1. **Narrow task**: Model only outputs ideology numbers (0-10). Not suitable for general political conversation.
2. **Canadian-specific**: Trained on CES 2021 under Trudeau government.
3. **Leader-specific**: Uses 2021 leader names (Trudeau, O'Toole, Singh). Would need adaptation for different elections.

## Citation

```bibtex
@software{ces-phase3-lora,
  title = {CES Phase 3 LoRA: Leader Affect and Policy Prediction},
  author = {Coburn, Del},
  year = {2025},
  url = {https://huggingface.co/baglecake/ces-phase3a-lora}
}
```

## Part of emile-GCE

This model is part of the [emile-GCE](https://github.com/delcoburn/emile-gce) project for Generative Computational Ethnography.