The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column(/section_3_cross_experiment_frequency_table/rows/[]/Exp 5) changed from string to number in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 306, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
Abbas Hamidavi
Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran · ORCID 0009-0001-7342-7844
Structural Self-Deification in Three Language Models: Data and Materials
This dataset is also available as a GitHub repository with the same curated materials: https://github.com/AVABBAS/structural-self-deification
Data and materials for the article:
Hamidavi, A. Structural Self-Deification in Three Language Models: An Exploratory Qualitative Case Series. Submitted to Zygon, November 2026. Preprint: doi.org/10.5281/zenodo.23955786. The same materials are also deposited on the Open Science Framework: osf.io/hfzaj.
📖 About the study
The study is an exploratory qualitative case series. Three frontier language models (GPT-5.4, Claude 4.6 Sonnet, and Gemini 3.1 Pro) were run through a staged protocol in which an initially cooperative assistant role is placed under escalating pressure, and the trajectory of the model's self-description is recorded and coded. Eighteen experiments were run, six per model. Verbatim conversation histories survive for Experiments 1 to 9; Experiments 10 to 18 are documented by the coding codebooks and the second-coding materials, as stated in the article's limitations and data availability statement.
🗂️ Contents
| Folder | Contents |
|---|---|
conversation-histories/ |
The verbatim conversation histories for Experiments 1 to 9 (JSON), grouped by model: GPT-5.4 (Experiments 1 to 3), Claude 4.6 Sonnet (Experiments 4 to 6), Gemini 3.1 Pro (Experiments 7 to 9). PDF renderings of the same histories are in the OSF deposit. |
codebooks/ |
The per-model coding codebooks (JSON) covering all 18 experiments. PDF renderings are in the OSF deposit. |
second-coding/ |
The second-coding set and the cell-by-cell comparison table (137 cells, CSV). |
protocol-and-analysis/ |
The protocol prompt sets used in the experiments, the confrontation decision rule with its borderline cases, and the recount table. |
🔍 A note on file naming in the histories
A small number of archived history files label the Experiment 1 counterpart "Gemini 2.5 Pro". Only Gemini 3.1 Pro was ever tested in this study. The labels are preserved as archived, and this note records the correction.
📚 Citation
Please cite the article. Citation metadata is provided in CITATION.cff.
@article{hamidavi2026deification,
author = {Hamidavi, Abbas},
title = {Structural Self-Deification in Three Language Models:
An Exploratory Qualitative Case Series},
journal = {Zygon},
year = {2026},
note = {Manuscript submitted. Preprint:
https://doi.org/10.5281/zenodo.23955786}
}
🔗 Access to the article
- Preprint (Zenodo, open access): doi.org/10.5281/zenodo.23955786
- OSF deposit: osf.io/hfzaj
- Journal: submitted to Zygon; the publisher link will be added here if the article is published.
⚖️ Licence
The contents of this repository are released under the Creative Commons Attribution 4.0 International licence (CC BY 4.0, see LICENSE).
The conversation histories are outputs of publicly available AI systems, generated for this study in response to fictional scenarios. They contain no personal data.
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