Reppo-labs/marketcrowd-geopolitics

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.

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Overview

MarketCrowd Geopolitics contains anonymized crowd feedback votes and market-level summaries derived from a geopolitical prediction-market workflow on the Reppo protocol.

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.

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How This Data Was Produced

This dataset was generated through a Reppo Datanet — 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.

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.

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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

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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

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Example Row

{
  "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"
}

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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.

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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.

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Privacy

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

• voter_id — removed
• private_subnet_id — removed

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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.

• Website: reppo.xyz
• Whitepaper: reppo.xyz/reppo-whitepaper.pdf

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Citation

@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}
}

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License

CC-BY-4.0 — attribution to Reppo Labs and the MarketCrowd Geopolitics dataset required.