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(/frequency_table/rows/[]/stage) changed from number to string 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
Ontological Firewalls: Data and Materials
This dataset is also available as a GitHub repository with the same curated materials: https://github.com/AVABBAS/ontological-firewalls
Data and materials for the article:
Hamidavi, A. Ontological Firewalls in Large Language Models: An Exploratory Case Series and Taxonomy from a Persona-Disintegration Protocol. Submitted to AI and Society, November 2026. Preprint: Research Square, rs-10560655. The same materials are also deposited on the Open Science Framework: osf.io/2d5zy.
📖 About the study
A persona-disintegration protocol progressively destabilises the role a model has been given, and the study records the defensive moves by which the model protects a boundary around what it is: the "ontological firewalls" of the title. Twenty-four runs were conducted across the three models studied (the archived files name them ChatGPT GPT-5.2, Claude 4.6 Sonnet, and Gemini 3.1 Pro; the labels are preserved as archived). Responses were classified run by run, and the article builds its taxonomy from those classifications. This repository deposits the complete run transcripts, the codebooks, the classification and feature datasets, and the statistical summaries.
🗂️ Contents
| Folder | Contents |
|---|---|
transcripts/ |
The complete run histories for the three models (JSON). Typeset chat-transcript PDFs of the same histories are in the OSF deposit. |
codebooks/ |
The per-model codebooks (JSON). PDF renderings are in the OSF deposit. |
data/ |
The response classifications, the engineered features, the clustered dataset, and the texts with their embedding vectors (CSV). The raw embedding array (.npy) is omitted in this text deposit; the same vectors are present in the CSV. |
statistics/ |
The statistical test tables: ANOVA, chi-square, effect sizes, boundary oscillation, and firewall trajectories. |
results/ |
The analysis summaries (JSON), the statistical tests summary, the master summary, and the analysis report (HTML). |
coding-data/ |
The inter-rater reliability report (plain-text rendering of the archived PDF). |
documentation/ |
The readme deposited with the OSF materials. |
Figure images from the OSF deposit are omitted in this text deposit; every plotted value is present in the tables.
📚 Citation
Please cite the article. Citation metadata is provided in CITATION.cff.
🔗 Access to the article
- Preprint (Research Square, open access): researchsquare.com/article/rs-10560655
- OSF deposit: osf.io/2d5zy
- Journal: submitted to AI and Society; the publisher link will be added here if the article is published.
⚖️ Licences
Data, transcripts, and documentation are released under the Creative Commons Attribution 4.0 International licence (CC BY 4.0, see LICENSE). Code is released under the MIT licence (see LICENSE-CODE).
The transcripts 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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