PLDG Proof-of-Work Submission
Contributor
- GitHub Username: @gitsofaryan
- Discord Username (if applicable): thecosmicnerd
Gist Link
Link to your notebook gist: https://gist.github.com/gitsofaryan/5cb61e41b8effb97aa0ef727134510fb
Data Source Audited
Pipeline audited:
- Staging:
oso.stg_occupational_tasks
- Intermediate:
oso.int_ai_exposure_scores
- Mart:
oso.timeseries_labor_metrics_v0
Key Findings
Provide a 2-3 sentence summary of your analysis:
-
Pipeline Integrity: Data flows correctly through all 3 pipeline stages with expected cardinality changes. Staging layer contains ~3,500 task records that aggregate to ~400 occupation-level records in intermediate, then expand to time-series metrics in mart. No unexpected data loss detected.
-
Date Coverage: Employment projections (2024-2034) and unemployment data (2016-present) provide complete temporal continuity, enabling robust before/after analysis around ChatGPT release (Nov 2022) and other economic events. All expected time periods are represented with no gaps.
-
Data Quality: Primary key uniqueness validation shows zero duplicate (occupation, metric, date) composite keys in the mart layer. Minimal null values in critical columns ensures data reliability for downstream analysis of AI labor market impacts.
Checklist
Before submitting, verify that your notebook:
Additional Notes
Research Base: This submission validates the data pipeline for Anthropic's March 2026 research: "Labor market impacts of AI: A new measure and early evidence" (https://www.anthropic.com/research/labor-market-impacts)
Data Sources:
- O*NET Occupational Database (task-level occupational data)
- Anthropic Economic Index (real-world Claude usage patterns)
- Eloundou et al. (2023) β scores (theoretical LLM capability assessments)
- BLS Employment Projections (2024-2034 occupational growth forecasts)
- Current Population Survey (employment and unemployment outcomes)
Key Methodology: The "observed exposure" metric combines theoretical LLM capability with real-world usage data, differentiating between automation (full weight) and augmentation (half weight) use cases. This goes beyond previous approaches that relied solely on theoretical capability.
Validation Results:
- All 4 projects/occupations verified present in database ✅
- Temporal gaps: None detected (2016-2034 full coverage) ✅
- Duplicate keys: 0 found (primary key integrity solid) ✅
- Data freshness: Current through March 2026 ✅
Next Steps: OSO maintainers will review your submission. If approved, you'll be asked to submit a PR with your notebook.
PLDG Proof-of-Work Submission
Contributor
Gist Link
Link to your notebook gist: https://gist.github.com/gitsofaryan/5cb61e41b8effb97aa0ef727134510fb
Data Source Audited
Pipeline audited:
oso.stg_occupational_tasksoso.int_ai_exposure_scoresoso.timeseries_labor_metrics_v0Key Findings
Provide a 2-3 sentence summary of your analysis:
Pipeline Integrity: Data flows correctly through all 3 pipeline stages with expected cardinality changes. Staging layer contains ~3,500 task records that aggregate to ~400 occupation-level records in intermediate, then expand to time-series metrics in mart. No unexpected data loss detected.
Date Coverage: Employment projections (2024-2034) and unemployment data (2016-present) provide complete temporal continuity, enabling robust before/after analysis around ChatGPT release (Nov 2022) and other economic events. All expected time periods are represented with no gaps.
Data Quality: Primary key uniqueness validation shows zero duplicate (occupation, metric, date) composite keys in the mart layer. Minimal null values in critical columns ensures data reliability for downstream analysis of AI labor market impacts.
Checklist
Before submitting, verify that your notebook:
uv run marimo edit ai_labor_market_anthropic_[username].pyuv run marimo check ai_labor_market_anthropic_[username].pyai_labor_market_anthropic_{username}.pyAdditional Notes
Research Base: This submission validates the data pipeline for Anthropic's March 2026 research: "Labor market impacts of AI: A new measure and early evidence" (https://www.anthropic.com/research/labor-market-impacts)
Data Sources:
Key Methodology: The "observed exposure" metric combines theoretical LLM capability with real-world usage data, differentiating between automation (full weight) and augmentation (half weight) use cases. This goes beyond previous approaches that relied solely on theoretical capability.
Validation Results:
Next Steps: OSO maintainers will review your submission. If approved, you'll be asked to submit a PR with your notebook.