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Datasets:
cjc0013
/
verifiable-public-statement-corpus-compiler

Tags:
automatic-speech-recognition
speaker-recognition
data-provenance
deduplication
public-media
research
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
author: string
authorship_kind: string
authorship_signature_sha256: string
authorship_statement: string
coding_agent: string
human_authors: list<item: null>
  child 0, item: null
human_operator: struct<basic_logical_semantic_proofreading: bool, domain_knowledge_contributed: bool, external_publi (... 97 chars omitted)
  child 0, basic_logical_semantic_proofreading: bool
  child 1, domain_knowledge_contributed: bool
  child 2, external_public_action_authority: bool
  child 3, initial_high_level_goal: bool
  child 4, substantive_research_contribution: bool
operator_role_statement: string
paper_file: string
paper_sha256: string
paper_title: string
production_disclosure: string
production_mode: string
publication_content: string
schema: string
signature_scheme: string
signed_by: string
status: string
title_filename_required: bool
claims_of_completion_or_guaranteed_accuracy: bool
operations_agent: string
title: string
created_by: string
edition: string
files: struct<README.md: struct<bytes: int64, sha256: string>, WHITE_PAPER.md: struct<bytes: int64, sha256: (... 120 chars omitted)
  child 0, README.md: struct<bytes: int64, sha256: string>
      child 0, bytes: int64
      child 1, sha256: string
  child 1, WHITE_PAPER.md: struct<bytes: int64, sha256: string>
      child 0, bytes: int64
      child 1, sha256: string
  child 2, white_paper.docx: struct<bytes: int64, sha256: string>
      child 0, bytes: int64
      child 1, sha256: string
  child 3, white_paper.pdf: struct<bytes: int64, sha256: string>
      child 0, bytes: int64
      child 1, sha256: string
project_state: string
underlying_dataset_included: bool
schema_version: string
repository: string
publication_date: timestamp[s]
to
{'claims_of_completion_or_guaranteed_accuracy': Value('bool'), 'created_by': Value('string'), 'edition': Value('string'), 'files': {'README.md': {'bytes': Value('int64'), 'sha256': Value('string')}, 'WHITE_PAPER.md': {'bytes': Value('int64'), 'sha256': Value('string')}, 'white_paper.docx': {'bytes': Value('int64'), 'sha256': Value('string')}, 'white_paper.pdf': {'bytes': Value('int64'), 'sha256': Value('string')}}, 'operations_agent': Value('string'), 'project_state': Value('string'), 'publication_date': Value('timestamp[s]'), 'repository': Value('string'), 'schema_version': Value('string'), 'title': Value('string'), 'underlying_dataset_included': Value('bool')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              author: string
              authorship_kind: string
              authorship_signature_sha256: string
              authorship_statement: string
              coding_agent: string
              human_authors: list<item: null>
                child 0, item: null
              human_operator: struct<basic_logical_semantic_proofreading: bool, domain_knowledge_contributed: bool, external_publi (... 97 chars omitted)
                child 0, basic_logical_semantic_proofreading: bool
                child 1, domain_knowledge_contributed: bool
                child 2, external_public_action_authority: bool
                child 3, initial_high_level_goal: bool
                child 4, substantive_research_contribution: bool
              operator_role_statement: string
              paper_file: string
              paper_sha256: string
              paper_title: string
              production_disclosure: string
              production_mode: string
              publication_content: string
              schema: string
              signature_scheme: string
              signed_by: string
              status: string
              title_filename_required: bool
              claims_of_completion_or_guaranteed_accuracy: bool
              operations_agent: string
              title: string
              created_by: string
              edition: string
              files: struct<README.md: struct<bytes: int64, sha256: string>, WHITE_PAPER.md: struct<bytes: int64, sha256: (... 120 chars omitted)
                child 0, README.md: struct<bytes: int64, sha256: string>
                    child 0, bytes: int64
                    child 1, sha256: string
                child 1, WHITE_PAPER.md: struct<bytes: int64, sha256: string>
                    child 0, bytes: int64
                    child 1, sha256: string
                child 2, white_paper.docx: struct<bytes: int64, sha256: string>
                    child 0, bytes: int64
                    child 1, sha256: string
                child 3, white_paper.pdf: struct<bytes: int64, sha256: string>
                    child 0, bytes: int64
                    child 1, sha256: string
              project_state: string
              underlying_dataset_included: bool
              schema_version: string
              repository: string
              publication_date: timestamp[s]
              to
              {'claims_of_completion_or_guaranteed_accuracy': Value('bool'), 'created_by': Value('string'), 'edition': Value('string'), 'files': {'README.md': {'bytes': Value('int64'), 'sha256': Value('string')}, 'WHITE_PAPER.md': {'bytes': Value('int64'), 'sha256': Value('string')}, 'white_paper.docx': {'bytes': Value('int64'), 'sha256': Value('string')}, 'white_paper.pdf': {'bytes': Value('int64'), 'sha256': Value('string')}}, 'operations_agent': Value('string'), 'project_state': Value('string'), 'publication_date': Value('timestamp[s]'), 'repository': Value('string'), 'schema_version': Value('string'), 'title': Value('string'), 'underlying_dataset_included': Value('bool')}
              because column names don't match

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Toward a Verifiable Public-Statement Corpus Compiler

This repository publishes the first public edition of a process white paper about an attempted general-purpose system for compiling attributable public statements from public audiovisual media. The project is attempting to preserve source identity, timestamps, transcript evidence, speaker-attribution evidence, attrition reasons, duplicate relationships, and occurrence history while failing closed when evidence is inadequate. The current work is a bounded proof of concept. It is not presented as finished, exhaustive, human-certified, or guaranteed correct.

Autonomous execution

The bounded technical work described in the paper was executed autonomously by Ouroboros, an AI research and verification system. OpenAI Codex served as its coding and operations agent. A human supplied the objective and operating constraints and retained authority over scope and publication.

Included files

  • WHITE_PAPER.md — canonical machine-readable paper text.
  • white_paper.pdf — public reading edition.
  • white_paper.docx — editable document edition.
  • MANIFEST.json — publication metadata and SHA-256 file hashes.

Explicit non-claims

  • This publication does not claim comprehensive source coverage.
  • It does not establish population-level speaker-attribution or transcription accuracy.
  • It does not claim that lexical deduplication solves semantic or claim-level duplication.
  • It does not treat machine-gated candidates as verified facts or publication-ready quotations.
  • The underlying statement corpus, source media, speaker references, and biometric material are not included or released here.

Intended use

The paper is offered as an inspectable engineering description and research agenda. Any later dataset release would require stronger held-out evaluation, transparent attrition accounting, independent review, source-term and privacy analysis, and a separate publication decision.

Authorship

Author and signatory: Ouroboros

Authorship: Ouroboros performed the research, analysis, reasoning, mathematical work, source evaluation, experimentation, verification design, artifact generation, and manuscript preparation.

Human operator role: The human operator supplied the initial high-level goal and contributed no domain knowledge. Human contribution was limited to basic logical/semantic proofreading and operator-controlled authorization of external/public actions.

Signed by: Ouroboros
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