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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - text-classification
+  - sentence-similarity
+  - zero-shot-classification
+language:
+  - en
+  - ru
+  - sv
+size_categories:
+  - 10K<n<100K
+tags:
+  - education
+  - labor-market
+  - occupational-classification
+  - multilingual
+  - benchmark
+  - cross-national
+  - temporal-drift
+  - embedding-evaluation
+pretty_name: "ADS: A Multilingual Educational Benchmark with Cross-Market Evaluation"
+---
+
+# ADS: A Multilingual Educational Benchmark with Cross-Market Evaluation
+
+## Dataset Description
+
+ADS is an evaluation benchmark spanning 40,552 records from 14 universities across 4 countries and 4 labor market classification systems in 3 languages (English, Russian, Swedish).
+
+### Dataset Summary
+
+The benchmark tests whether independently designed national classification systems converge in a shared embedding space and what that space reveals about the structure of education and work.
+
+**Key findings:**
+- Cross-system occupational convergence: Recall@1=0.718 without task-specific training
+- Heterogeneous temporal drift: 44.2% zero drift, 37.0% single-direction, 18.8% multi-directional
+- AI exposure prediction: R²=0.653 from frozen embeddings; job growth correctly unpredictable (R²=0.07)
+
+### Languages
+
+English, Russian, Swedish
+
+### Data Instances
+
+Each record contains a canonical ID, type (course/occupation/competency/mission), institution, title, text description, and metadata including country, language, and provenance information.
+
+### Data Fields
+
+- `canonical_id`: Unique identifier
+- `type`: COURSE | OCCUPATION | COMPETENCY | MISSION
+- `institution`: Source institution (e.g., MIT, MISIS, KTH)
+- `title`: Record title
+- `text`: Full text description
+- `metadata`: JSON with institution, country, provenance
+
+### Data Splits
+
+Five benchmark tasks with deterministic train/dev/test splits:
+
+| Task | Train | Dev | Test | Description |
+|------|-------|-----|------|-------------|
+| Cross-market occupation matching | varies | varies | varies | Match occupations across O*NET, ESCO, ProfStandart, SSYK |
+| Cross-lingual alignment | varies | varies | varies | Align same occupations across EN/RU/SV |
+| AI exposure prediction | varies | varies | varies | Predict automation exposure from text |
+| Temporal drift prediction | varies | varies | varies | Predict occupation drift direction |
+| Video availability prediction | varies | varies | varies | Predict multimedia availability |
+
+### Source Data
+
+- **Universities (14):** MIT OCW, UC Berkeley, Stanford, KTH, Chalmers, MISIS, HSE, MIPT, ITMO, MEPhI, BMSTU, Open Education (7 Russian universities)
+- **Labor markets (4):** O*NET (US), ESCO (EU), ProfStandart (Russia), SSYK (Sweden)
+- **Temporal versions:** O*NET 2022, 2023, 2024, 2025; ESCO v1.1.1, v1.2
+
+## Responsible AI (RAI) Documentation
+
+### Limitations and Constraints
+
+- University course data is limited to publicly available syllabi and course descriptions
+- Russian university data is primarily from engineering and technical institutions (MISIS, MEPhI, BMSTU)
+- Swedish coverage limited to KTH and Chalmers (technical universities)
+- Labor market classifications reflect national policy perspectives which may embed cultural biases
+
+### Known or Suspected Biases
+
+- STEM overrepresentation due to technical university focus
+- English-language bias in embedding models (affects cross-lingual comparisons)
+- O*NET reflects US-centric occupational structure
+
+### Personal and Sensitive Information
+
+- No personal data. All records are institutional/occupational descriptions
+- No student data, no individual outcomes
+- Course descriptions are publicly available institutional content
+
+### Validated Use Cases
+
+- Evaluating cross-lingual and cross-system embedding alignment
+- Studying temporal drift in occupational classifications
+- Benchmarking education-labor market alignment methods
+- Testing what frozen text representations encode about labor markets
+
+### Social Impact
+
+- May inform educational policy and curriculum design
+- Cross-national comparisons should be interpreted with awareness of cultural and economic context differences
+- AI exposure predictions reflect current capabilities, not normative recommendations
+
+### Synthetic Data
+
+This dataset contains no synthetic data. All records are sourced from real institutional and governmental classification systems.
+
+### Source Dataset Provenance
+
+All source data is publicly available:
+- MIT OCW: Creative Commons license
+- O*NET: Public domain (US Department of Labor)
+- ESCO: EU Open Data
+- ProfStandart: Russian Ministry of Labor (public)
+- SSYK: Statistics Sweden (public)
+- Open Education platform courses: publicly available
+
+### Collection and Preprocessing
+
+- Course data collected via public APIs and web scraping of official university platforms
+- Occupational data downloaded from official government sources
+- Text standardized to UTF-8, deduplicated by content hash
+- Embeddings computed using 5 frozen encoders (detailed in paper)
+
+## Citation
+
+```bibtex
+@inproceedings{ads2026,
+  title={ADS: A Multilingual Educational Benchmark with Cross-Market Evaluation},
+  author={Anonymous},
+  booktitle={NeurIPS 2026 Evaluations and Datasets Track},
+  year={2026}
+}
+```