| --- |
| 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} |
| } |
| ``` |
| |
