README.md

---
license: cc-by-4.0
language:
- en
tags:
- prediction-markets
- crowdsourcing
- geopolitics
- forecasting
- reasoning
- ai-evaluation
- agents
- uncertainty
- calibration
- human-feedback
- stake-assured
- rlhf
task_categories:
- text-classification
- question-answering
- text-generation
pretty_name: MarketCrowd Geopolitics
size_categories:
- n<1K
configs:
- config_name: votes
  data_files:
    - split: train
      path: data/train.jsonl
- config_name: market_summary
  data_files:
    - split: train
      path: data/market_summary.jsonl
---

# MarketCrowd Geopolitics

**The first open dataset produced via stake-assured human feedback (SAHF)** — preference signals crowdsourced through capital-at-risk voting on geopolitical AI reasoning.

---

## Overview

MarketCrowd Geopolitics contains anonymized crowd feedback votes and market-level summaries derived from a geopolitical prediction-market workflow on the [Reppo protocol](https://reppo.xyz).

Unlike standard annotation datasets where labelers are paid per task, every signal in this dataset was produced by voters who locked $REPPO tokens as economic collateral — meaning their judgments carry capital at risk, not just attention.

This is an **initial seed release** covering ~6 weeks of activity across 51 geopolitical markets (March–April 2026). The underlying dataset updates continuously as new epochs settle on the Reppo protocol.

---

## How This Data Was Produced

This dataset was generated through a [Reppo Datanet](https://reppo.xyz/reppo-whitepaper.pdf) — an on-chain data market where:

1. **Publishers** submit geopolitical analyses as Pods (units of content tied to a market question)
2. **Voters** lock $REPPO tokens to receive veREPPO voting power, then allocate that power to evaluate each Pod — positively or negatively
3. **Linear decay** rewards early, independent judgment: votes cast earlier in the 48-hour epoch carry more weight than late votes, reducing herd behavior
4. **Net-vote settlement** at epoch close determines which Pods enter the curated dataset (positive net vote = accepted; negative = filtered out, publisher zeroed)
5. The resulting consensus is a **synthetic oracle** — no external arbiter required

This mechanism is described in full in the [Reppo whitepaper](https://reppo.xyz/reppo-whitepaper.pdf).

### Why stake-assured feedback differs from standard annotation

| Property | Paid annotation (e.g. Scale AI) | Reppo SAHF |
|---|---|---|
| Voter incentive | Payment per task | Emissions + locked capital at risk |
| Sybil resistance | Identity verification | Capital requirement (splitting stake doesn't increase power) |
| Disagreement handling | Averaged out | Explicit signal via net-vote mechanism |
| Quality metric | Inter-annotator agreement | Economic Value of Feedback (EVOF) |
| Data staleness | Static snapshot | Continuously updated every 48 hours |

### Voting power concentration

The `voting_power` field is derived from locked $REPPO × lock duration (non-linear). In this release, voting power ranges from 2,471 to 3,013,340 with a median of 64,110. The top 3 wallets account for ~13% of total voting power — relatively distributed for a token-weighted system. The Reppo protocol further mitigates concentration via square-root dampening in the EVOF metric.

---

## What Is Included

| File | Rows | Description |
|---|---:|---|
| `data/train.jsonl` | 160 | Row-level anonymized weighted crowd feedback votes |
| `data/train.csv` | 160 | CSV version of the row-level data |
| `data/market_summary.jsonl` | 51 | Aggregated summaries by geopolitical question/market |
| `data/market_summary.csv` | 51 | CSV version of the market-level summary data |
| `DATA_DICTIONARY.md` | — | Field-level documentation |
| `dataset_schema.json` | — | Machine-readable schema |

---

## Dataset Structure

### `data/train.jsonl` — row-level votes

Each row is an anonymized feedback vote attached to a geopolitical prediction-market question.

| Field | Type | Description |
|---|---|---|
| `id` | string | Stable public row ID |
| `source_record_id` | string | Original feedback record ID |
| `record_type` | string | Always `crowd_feedback_vote` |
| `question` | string | Geopolitical market/question title |
| `category` | string | Always `geopolitics` in this release |
| `market_id` | string | Original market/Pod ID |
| `epoch` | int | 48-hour epoch in which the vote was cast (higher = more recent) |
| `feedback` | string | Qualitative label and/or freeform comment. Format: `"Label1, Label2 - optional comment"` |
| `vote` | string | `up_vote` or `down_vote` |
| `up_vote` | bool | Boolean vote direction |
| `votes` | int | Raw on-chain vote count. Distinct from `voting_power` — will be 0 for many rows |
| `voting_power` | int | veREPPO weight — the economically meaningful signal. Derived from $REPPO locked × lock duration |
| `created_at` | string | Feedback creation timestamp |
| `updated_at` | string | Feedback update timestamp |
| `deleted` | bool | Source deletion flag |
| `version` | int | Source version field |
| `split` | string | Dataset split |

### `data/market_summary.jsonl` — market-level aggregates

One row per geopolitical question, summarizing all votes for that market.

| Field | Type | Description |
|---|---|---|
| `id` | string | Stable public summary ID |
| `record_type` | string | Always `market_feedback_summary` |
| `question` | string | Geopolitical market/question title |
| `category` | string | Topic category |
| `market_id` | string | Original market/Pod ID |
| `num_feedback_votes` | int | Total number of feedback votes |
| `up_votes` | int | Count of positive votes |
| `down_votes` | int | Count of negative votes |
| `up_vote_share` | float | Fraction of votes that were positive |
| `total_voting_power` | int | Sum of voting power across all votes |
| `median_voting_power` | float | Median voting power per vote |
| `top_feedback_tags` | list | Most common feedback labels |
| `first_seen_at` | string | Earliest feedback timestamp |
| `last_seen_at` | string | Latest feedback timestamp |
| `split` | string | Dataset split |

---

## Example Row

```json
{
  "id": "mcg_vote_000001",
  "source_record_id": "cmndwvyey0001kz04mn8e5fmp",
  "record_type": "crowd_feedback_vote",
  "question": "Hormuz Updates",
  "category": "geopolitics",
  "market_id": "cmn9plf2k0001kz040cuuizdq",
  "epoch": 64,
  "feedback": "High quality -",
  "vote": "up_vote",
  "up_vote": true,
  "votes": 0,
  "voting_power": 1592667,
  "created_at": "2026-03-31 01:01:42.778",
  "updated_at": "2026-03-31 01:01:42.778",
  "deleted": false,
  "version": 1,
  "split": "train"
}
```

---

## Suggested Uses

This dataset is best treated as a **feedback-signal and question-quality dataset**. It should not be used as a source of resolved geopolitical ground truth.

**Recommended training setup:**

| Component | Field |
|---|---|
| Input | `question` + `feedback` |
| Label | `up_vote` or `vote` |
| Sample weight | `voting_power` |

**Agent training use cases:**

| Agent task | Input | Signal |
|---|---|---|
| Question quality scoring | Market question | `up_vote_share`, `voting_power`, feedback labels |
| Market triage | Question + feedback | Aggregated market summary |
| Feedback classification | Feedback text | Vote direction or feedback category |
| Signal summarization | Question + row-level votes | Market-level summary |
| Forecasting workflow support | Proposed market | Crowd-perceived usefulness and clarity |

**Other applications:** text classification, weak supervision, market ranking, crowd-signal summarization, geopolitical forecasting prompt evaluation, prediction-market-based data research.

---

## Limitations

- **Size**: 160 votes across 51 markets from a 6-week window. This is a seed release — not yet suitable as a standalone benchmark.
- **Single domain**: All records are `category: geopolitics` from one Datanet. Cross-domain generalization should not be assumed.
- **Feedback field**: Mixes structured tags with freeform comments. Preprocessing recommended before classification tasks.
- **No resolved outcomes**: This is a preference/feedback dataset, not a ground-truth geopolitical dataset. Event resolution is not included.
- **`votes` vs `voting_power`**: The `votes` field is 0 for most rows — it reflects a raw on-chain count that was not recorded for all votes. Use `voting_power` as the primary signal.

---

## Privacy

The source data contained direct voter and subnet identifiers. These were removed before this public release:

- `voter_id` — removed
- `private_subnet_id` — removed

---

## About Reppo

Reppo is a protocol for tokenized, continuously curated data production for reinforcement learning. Datanets are on-chain data markets where publishers contribute content and veREPPO holders provide stake-assured quality assessments. Datasets update every 48 hours and are available for subscription via [repo.exchange](https://repo.exchange).

- Website: [reppo.xyz](https://reppo.xyz)
- Whitepaper: [reppo.xyz/reppo-whitepaper.pdf](https://reppo.xyz/reppo-whitepaper.pdf)

---

## Citation

```bibtex
@dataset{reppo_marketcrowd_geopolitics_2026,
  author    = {Reppo Labs},
  title     = {MarketCrowd Geopolitics: Stake-Assured Human Feedback on Geopolitical AI Reasoning},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/Reppo-labs/marketcrowd-geopolitics}
}
```

---

## License

[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) — attribution to Reppo Labs and the MarketCrowd Geopolitics dataset required.