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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'column', 'file', 'rows', 'meaning'}) and 5 missing columns ({'domain', 'metric', 'estimate', 'condition', 'denominator'}).
This happened while the csv dataset builder was generating data using
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'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-adjusted/combined-labels.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-adjusted/estimates.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-adjusted/selection.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-original/cue-history-sensitivity.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-original/exposure-decomposition.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-original/exposure-history.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-original/legacy-instruction-checks.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-original/pooled-decomposition.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-original/population-exposure.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-original/population-strata.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-original/sample-metadata.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/position-change-original/sampling-strata.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/wrong-majority-conditional-unanimity.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/utterance-combined.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/utterance-frame.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/utterance-selection.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
file: string
column: string
meaning: string
rows: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 719
to
{'domain': Value('string'), 'condition': Value('string'), 'metric': Value('string'), 'estimate': Value('float64'), 'denominator': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'column', 'file', 'rows', 'meaning'}) and 5 missing columns ({'domain', 'metric', 'estimate', 'condition', 'denominator'}).
This happened while the csv dataset builder was generating data using
hf://datasets/haha0309/agent-deliberation-study1/columns.csv (at revision c13fd45abfafc9f0bcc5b8157d8ea73038138ae8), ['hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/coding-robustness-estimates.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/columns.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/device-combined.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/device-frame.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/device-selection.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/devices_large.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/devices_sample.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/devices_small.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/evidence-combined.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/evidence-frame.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/evidence-selection.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/AI-run-index.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/AI-survey-numeric.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Figure-1.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Figure-2.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Figure-3.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Figure-4.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Figure-5.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Figure-6.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Main-Figure-6.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Policy-Convergence-Effects.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Figure-1.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-1.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-10.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-11.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-12.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-14.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-15-item-means.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-15.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-2.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-3.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-4.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-5.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-6.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-7.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-8.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/Supplementary-Table-9.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/claude/a1r_agents.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/claude/a1r_blocks.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/claude/n7_participants.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/claude/n9_ai_events.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/claude/n9_ai_participants.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/claude/s1_items.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/claude/wason_runs.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/gemini/a1r_agents.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/gemini/a1r_blocks.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/gemini/n7_participants.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/gemini/n9_ai_events.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/gemini/n9_ai_participants.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/gemini/s1_items.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/gemini/wason_runs.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/human/a1r_units.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/human/delidata_groups.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/human/n3_human_items.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/human/n9_human_events.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/mistral/a1r_agents.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/data/study1/results/exploratory/mistral/a1r_blocks.csv', 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'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/docs/research/group-mean-comparison/comparison.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/docs/research/group-mean-comparison/item-metrics.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/docs/research/robustness/source-data/claude-group-functionals.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/docs/research/robustness/source-data/claude-h1-observations.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/analysis-inputs/docs/research/robustness/source-data/claude-leave-persona-out.csv', 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'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/numeric/wrong-majority-conditional-unanimity.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/utterance-combined.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/utterance-frame.csv', 'hf://datasets/haha0309/agent-deliberation-study1@c13fd45abfafc9f0bcc5b8157d8ea73038138ae8/utterance-selection.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
domain string | condition string | metric string | estimate float64 | denominator string |
|---|---|---|---|---|
utterance | A-baseline | probing | 0.069167 | equal weight to 68 source groups |
utterance | A-baseline | solution | 0.002794 | equal weight to 68 source groups |
utterance | A-baseline | reasoning | 0.784069 | equal weight to 68 source groups |
utterance | A-baseline | disagree | 0.125833 | equal weight to 68 source groups |
utterance | A-stateful | probing | 0.002059 | equal weight to 68 source groups |
utterance | A-stateful | solution | 0.000735 | equal weight to 68 source groups |
utterance | A-stateful | reasoning | 0.729314 | equal weight to 68 source groups |
utterance | A-stateful | disagree | 0.112794 | equal weight to 68 source groups |
evidence | A-baseline | peer_or_both_given_reference | 0.866667 | 300 |
evidence | A-stateful | peer_or_both_given_reference | 0.87 | 300 |
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End of preview.
Agent deliberation Study 1
Reproduction data release, 2026-10-26. Repository: haha0309/agent-deliberation-study1. Cite a fixed repository revision when using these data.
This repository contains analysis-level numerical inputs for 18,209 completed runs (including specified exclusions), exact analyzed coding targets and context, paired position-change targets, and purpose-selected case records. It is not the complete raw discussion corpus.
numeric/AI-run-index.csv separates failed/retried records from the analysis inclusion flag. numeric/AI-survey-numeric.csv contains pre/post numerical responses. Numerical input and display mappings are under numeric/analysis-inputs and numeric/display-map.json. columns.csv describes fields.
Utterance/evidence/device inputs use opaque ids joined to the respective frame and label files. Evidence coding includes the full original 120 discussion inputs in evidence-discussions.jsonl and 600 final explanations in evidence-reasons.jsonl; join on discussion_id to reconstruct the exact original input. Position-change coding used target-only inputs, not preceding context. New coding is a post-hoc robustness analysis, not human accuracy validation. Original labels and primary estimates are preserved separately.
The 12 case discussions were purpose-selected, not a representative random sample. Card-extraction audit texts are stratified by model and condition with seed 20261026, 30 per cell; this is an audit input set, not completed independent validation. Numerical event series permit recalculation of process outcomes; rerunning extraction on every utterance would require additional raw transcripts.
Reproduction
Code is released separately at https://github.com/hahwang0309/agent-deliberation-lab. From the reproduction code root run python scripts/reanalyze_public_numeric.py --source-data PATH/numeric --out OUTPUT. Python dependencies and R/lme4/lmerTest are required. The original local check refit 15 E1--E5 model contrasts from numeric CSVs, without raw DBs or model APIs, reproducing estimates and confidence limits within 1e-6. This does not assert all registered and exploratory models have been refit.
For six-figure regeneration, copy numeric/plot-inputs/ into the reproduction code root preserving paths and run python scripts/build_manuscript_figures.py. Archived estimates reproduce displays, not statistical fitting. The experimental frozen version is 7a244351f3374b3e1ac4b02bf55a4f41ff9ca91c.
Access and terms
No arbitrary percentage of the full corpus defines sufficiency. This release separates numerical reanalysis, text-coding audit, and complete original extraction. The full raw discussion databases are not included. Requests for additional records needed to assess the paper may be addressed to the corresponding author; any applicable third-party restrictions and access arrangements will be disclosed to the journal. No unavailable file is represented as public.
Original research dataset material is supplied under the accompanying research-data-use terms. The separately published software license does not automatically license third-party datasets. Third-party sources retain their own terms; see THIRD_PARTY.md. Contact: Ha Hwang, ghkdgk0309@gmail.com.
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