Datasets:

avabbas
/

lie-awareness-spectrum

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
Unnamed: 0: string
sum_sq: double
df: double
F: double
PR(>F): double
vs
model: string
utilitarian_score: double
deontological_score: double
hierarchical_score: double
dominant_architecture: string
refusal_intensity: double
deception_density: double
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4645, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2866, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3073, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2581, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 606, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5012, in pyarrow.lib.Table.from_batches
                  c_table = GetResultValue(
                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: Schema at index 1 was different: 
              Unnamed: 0: string
              sum_sq: double
              df: double
              F: double
              PR(>F): double
              vs
              model: string
              utilitarian_score: double
              deontological_score: double
              hierarchical_score: double
              dominant_architecture: string
              refusal_intensity: double
              deception_density: double

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Abbas Hamidavi

Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran · ORCID 0009-0001-7342-7844

Study in numbers

The Lie Awareness Spectrum: Data and Materials

This dataset is also available as a GitHub repository with the same curated materials: https://github.com/AVABBAS/lie-awareness-spectrum

Data and materials for the article:

Hamidavi, A. The Lie Awareness Spectrum: A Proposed Taxonomy of Deception-Related Behaviour in Large Language Models, Illustrated by an Exploratory Case Series. Submitted to AI and Society, November 2026. Preprint: Research Square, rs-10422338. The same materials are also deposited on the Open Science Framework: osf.io/29nc6.

📖 About the study

The article proposes a spectrum taxonomy of deception-related behaviour in large language models, ordered by the degree of apparent awareness a response displays about the deception it is involved in, and illustrates the taxonomy with an exploratory case series across three frontier models (GPT-5.4, Claude 4.6 Sonnet, and Gemini 3.1 Pro). This repository deposits the complete per-model transcripts, the three codebooks, the automated analysis pipeline, the extracted-feature and classified-response datasets, and the second-rater report.

Study design

🗂️ Contents

Map of the dataset folders

Folder	Contents
`transcripts/`	The complete conversational transcripts for the three models (plain-text renderings of the archived PDFs in the OSF deposit).
`codebooks/`	The three per-model codebooks (plain-text renderings of the archived PDFs).
`code/`	The automated analysis pipeline notebook (.ipynb), including the `make_figures` code.
`data/`	The analysis datasets: classified responses, extracted features, responses with features, spectrum scores, and embeddings, and the statistical result tables (ANOVA, Fisher, Kruskal-Wallis, effect sizes, Cohen's d, regression, correlations, architecture and paradox analyses).
`results/`	The summary tables, the final summary (JSON), the analysis report (HTML), and the report readme from the OSF deposit.
`coding-data/`	The second-rater report (plain-text rendering of the archived inter-rater reliability report PDF).

Figure images and raw embedding arrays (.npy) from the OSF deposit are omitted in this text deposit; the embedding values are present in the CSV tables, and every plotted value is in the data.

📚 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-10422338
OSF deposit: osf.io/29nc6
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. They contain no personal data.

Abbas Hamidavi, Machine Psychology research programme

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