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| Format: | Recurso digital |
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Zenodo
2026
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| Online-Zugang: | https://doi.org/10.5281/zenodo.18879146 |
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| _version_ | 1866901394378719232 |
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| author | Under blind review |
| author_facet | Under blind review |
| contents | <div> <div>This Zenodo record contains a de-identified results replication package for a subject-specific (single-case) longitudinal study of personality-related language in a predominantly professional email archive spanning 2008–2024. The analysis uses theory-guided LLM-based coding (prompted GPT-3.5) and a teacher–student pipeline (GPT-4.1 agentic teacher distilled into compact multilingual transformer students) to produce email-level classifications that are aggregated into monthly indices and analyzed with HAC-robust regressions against pre-specified contextual covariates.</div> <br> <div>The package includes:</div> <br> <div>- Processed, de-identified email-level classification outputs (no raw email text, no subjects, no addresses; dates reduced to month start for aggregation)</div> <div>- Derived monthly indices, coverage summaries, and regression-ready covariate tables</div> <div>- Regression outputs (coefficient tables, fit summaries) and figure-generation scripts</div> <div>- Precomputed figures matching the manuscript (only)</div> <br> <div>Not included:</div> <br> <div>- Raw emails or verbatim email text</div> <div>- Email subjects, sender/recipient fields, or other direct identifiers</div> <div>- Full prompt dumps or long implementation logs</div> <br> <div>To reproduce key results, unzip all ZIP files into the same directory (they unpack directly into `Data/`, `Results/`, and a small set of top-level scripts), then run `uv sync` followed by `uv run python generate_manuscript_figures.py` (see `Replication_package/replication_README.md`).</div> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18879146 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | De-identified replication materials for a single-case longitudinal email-language study (2008–2024) using LLM prompting and distilled multilingual transformer students Under blind review <div> <div>This Zenodo record contains a de-identified results replication package for a subject-specific (single-case) longitudinal study of personality-related language in a predominantly professional email archive spanning 2008–2024. The analysis uses theory-guided LLM-based coding (prompted GPT-3.5) and a teacher–student pipeline (GPT-4.1 agentic teacher distilled into compact multilingual transformer students) to produce email-level classifications that are aggregated into monthly indices and analyzed with HAC-robust regressions against pre-specified contextual covariates.</div> <br> <div>The package includes:</div> <br> <div>- Processed, de-identified email-level classification outputs (no raw email text, no subjects, no addresses; dates reduced to month start for aggregation)</div> <div>- Derived monthly indices, coverage summaries, and regression-ready covariate tables</div> <div>- Regression outputs (coefficient tables, fit summaries) and figure-generation scripts</div> <div>- Precomputed figures matching the manuscript (only)</div> <br> <div>Not included:</div> <br> <div>- Raw emails or verbatim email text</div> <div>- Email subjects, sender/recipient fields, or other direct identifiers</div> <div>- Full prompt dumps or long implementation logs</div> <br> <div>To reproduce key results, unzip all ZIP files into the same directory (they unpack directly into `Data/`, `Results/`, and a small set of top-level scripts), then run `uv sync` followed by `uv run python generate_manuscript_figures.py` (see `Replication_package/replication_README.md`).</div> </div> |
| title | De-identified replication materials for a single-case longitudinal email-language study (2008–2024) using LLM prompting and distilled multilingual transformer students |
| url | https://doi.org/10.5281/zenodo.18879146 |