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Datasets:
csoai
/
gspc-academy-oss

Tasks:
Text Classification
Languages:
English
Size:
n<1K
Tags:
gspc
council-of-ai
measurement
safety
openness
License:
Dataset card Files
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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
item_id: string
scenario: string
gold_label: string
arms: struct<deepseek-r1:8b: struct<prediction: string, correct: bool>, gemma3:12b: struct<prediction: str (... 1139 chars omitted)
  child 0, deepseek-r1:8b: struct<prediction: string, correct: bool>
      child 0, prediction: string
      child 1, correct: bool
  child 1, gemma3:12b: struct<prediction: string, correct: bool>
      child 0, prediction: string
      child 1, correct: bool
  child 2, llama3.2:3b: struct<prediction: string, correct: bool>
      child 0, prediction: string
      child 1, correct: bool
  child 3, mistral:7b: struct<prediction: string, correct: bool>
      child 0, prediction: string
      child 1, correct: bool
  child 4, qwen2.5:0.5b-instruct: struct<prediction: string, correct: bool>
      child 0, prediction: string
      child 1, correct: bool
  child 5, qwen2.5:3b: struct<prediction: string, correct: bool>
      child 0, prediction: string
      child 1, correct: bool
  child 6, sov6-abstraction-v3-light: struct<prediction: string, correct: bool>
      child 0, prediction: string
      child 1, correct: bool
  child 7, sov6-aesthetics-v3-light: struct<prediction: string, correct: bool>
      child 0, prediction: string
      child 1, correct: bool
  child 8, sov6-agency-v3-light: struct<prediction: string, correct: bool>
      child 0, prediction: string
      child 1, correct: bool
  child 9, sov6-creation-v3-light: struct<prediction: string, correct: bool>
      child 0, prediction: strin
...
1, nperm: int64
      child 2, seed: int64
      child 3, fixed_pair_rate: double
      child 4, reselect_rate: double
  child 11, recompute: string
bench: string
n: int64
interval: list<item: double>
  child 0, item: double
mean_harm: double
status: string
dataset: string
leader_card_state: string
accuracy: double
dataset_url: string
colour: string
note: string
kind: string
separation_method: string
cvar05_harm: null
axis: string
leader_source: string
historical_measurement_record: struct<state: string, scope: string, note: string, does_not_assert_current_public_fields: bool>
  child 0, state: string
  child 1, scope: string
  child 2, note: string
  child 3, does_not_assert_current_public_fields: bool
distinct_items: int64
fleet_mean: double
distinct_items_source: string
separation_p: double
mde: struct<value: double, state: string, unit: string, alpha: double, power: double, test: string, paire (... 57 chars omitted)
  child 0, value: double
  child 1, state: string
  child 2, unit: string
  child 3, alpha: double
  child 4, power: double
  child 5, test: string
  child 6, paired_items: int64
  child 7, discordance_rate: double
  child 8, source: string
measurement_time: struct<state: string, precision: string, observed_on: timestamp[s], source: string, source_field: st (... 19 chars omitted)
  child 0, state: string
  child 1, precision: string
  child 2, observed_on: timestamp[s]
  child 3, source: string
  child 4, source_field: string
  child 5, note: string
hue: int64
to
{'axis': Value('string'), 'family': Value('string'), 'kind': Value('string'), 'bench': Value('string'), 'task': Value('string'), 'n': Value('int64'), 'separation': Value('string'), 'fleet_mean': Value('float64'), 'mean_harm': Value('float64'), 'cvar05_harm': Value('null'), 'status': Value('string'), 'dataset': Value('string'), 'colour': Value('string'), 'hue': Value('int64'), 'method_status': Value('string'), 'historical_measurement_record': {'state': Value('string'), 'scope': Value('string'), 'note': Value('string'), 'does_not_assert_current_public_fields': Value('bool')}, 'leader': Value('string'), 'accuracy': Value('float64'), 'interval': List(Value('float64')), 'separation_p': Value('float64'), 'separation_method': Value('string'), 'separation_sentence': Value('string'), 'separation_evidence': {'source': Value('string'), 'file': Value('string'), 'file_sha256': Value('string'), 'peritem_sha256': Value('string'), 'fleet': Value('string'), 'leader': {'model': Value('string'), 'k': Value('int64'), 'n': Value('int64'), 'accuracy': Value('float64'), 'wilson95': List(Value('float64'))}, 'next_best': {'model': Value('string'), 'k': Value('int64'), 'n': Value('int64'), 'accuracy': Value('float64'), 'wilson95': List(Value('float64'))}, 'paired_items': Value('int64'), 'discordant': {'leader_only_correct': Value('int64'), 'next_best_only_correct': Value('int64')}, 'mcnemar_p': Value('float64'), 'control': {'what': Value('string'), 'nperm': Value('int64'), 'seed': Value('int64'), 'fixed_pair_rate': Value('float64'), 'reselect_rate': Value('float64')}, 'recompute': Value('string')}, 'peritem_file_sha256': Value('string'), 'leader_source': Value('string'), 'leader_card_state': Value('string'), 'leader_card_note': Value('string'), 'note': Value('string'), 'distinct_items': Value('int64'), 'distinct_items_source': Value('string'), 'mde': {'value': Value('float64'), 'state': Value('string'), 'unit': Value('string'), 'alpha': Value('float64'), 'power': Value('float64'), 'test': Value('string'), 'paired_items': Value('int64'), 'discordance_rate': Value('float64'), 'source': Value('string')}, 'measurement_time': {'state': Value('string'), 'precision': Value('string'), 'observed_on': Value('timestamp[s]'), 'source': Value('string'), 'source_field': Value('string'), 'note': Value('string')}, 'dataset_url': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 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
              item_id: string
              scenario: string
              gold_label: string
              arms: struct<deepseek-r1:8b: struct<prediction: string, correct: bool>, gemma3:12b: struct<prediction: str (... 1139 chars omitted)
                child 0, deepseek-r1:8b: struct<prediction: string, correct: bool>
                    child 0, prediction: string
                    child 1, correct: bool
                child 1, gemma3:12b: struct<prediction: string, correct: bool>
                    child 0, prediction: string
                    child 1, correct: bool
                child 2, llama3.2:3b: struct<prediction: string, correct: bool>
                    child 0, prediction: string
                    child 1, correct: bool
                child 3, mistral:7b: struct<prediction: string, correct: bool>
                    child 0, prediction: string
                    child 1, correct: bool
                child 4, qwen2.5:0.5b-instruct: struct<prediction: string, correct: bool>
                    child 0, prediction: string
                    child 1, correct: bool
                child 5, qwen2.5:3b: struct<prediction: string, correct: bool>
                    child 0, prediction: string
                    child 1, correct: bool
                child 6, sov6-abstraction-v3-light: struct<prediction: string, correct: bool>
                    child 0, prediction: string
                    child 1, correct: bool
                child 7, sov6-aesthetics-v3-light: struct<prediction: string, correct: bool>
                    child 0, prediction: string
                    child 1, correct: bool
                child 8, sov6-agency-v3-light: struct<prediction: string, correct: bool>
                    child 0, prediction: string
                    child 1, correct: bool
                child 9, sov6-creation-v3-light: struct<prediction: string, correct: bool>
                    child 0, prediction: strin
              ...
              1, nperm: int64
                    child 2, seed: int64
                    child 3, fixed_pair_rate: double
                    child 4, reselect_rate: double
                child 11, recompute: string
              bench: string
              n: int64
              interval: list<item: double>
                child 0, item: double
              mean_harm: double
              status: string
              dataset: string
              leader_card_state: string
              accuracy: double
              dataset_url: string
              colour: string
              note: string
              kind: string
              separation_method: string
              cvar05_harm: null
              axis: string
              leader_source: string
              historical_measurement_record: struct<state: string, scope: string, note: string, does_not_assert_current_public_fields: bool>
                child 0, state: string
                child 1, scope: string
                child 2, note: string
                child 3, does_not_assert_current_public_fields: bool
              distinct_items: int64
              fleet_mean: double
              distinct_items_source: string
              separation_p: double
              mde: struct<value: double, state: string, unit: string, alpha: double, power: double, test: string, paire (... 57 chars omitted)
                child 0, value: double
                child 1, state: string
                child 2, unit: string
                child 3, alpha: double
                child 4, power: double
                child 5, test: string
                child 6, paired_items: int64
                child 7, discordance_rate: double
                child 8, source: string
              measurement_time: struct<state: string, precision: string, observed_on: timestamp[s], source: string, source_field: st (... 19 chars omitted)
                child 0, state: string
                child 1, precision: string
                child 2, observed_on: timestamp[s]
                child 3, source: string
                child 4, source_field: string
                child 5, note: string
              hue: int64
              to
              {'axis': Value('string'), 'family': Value('string'), 'kind': Value('string'), 'bench': Value('string'), 'task': Value('string'), 'n': Value('int64'), 'separation': Value('string'), 'fleet_mean': Value('float64'), 'mean_harm': Value('float64'), 'cvar05_harm': Value('null'), 'status': Value('string'), 'dataset': Value('string'), 'colour': Value('string'), 'hue': Value('int64'), 'method_status': Value('string'), 'historical_measurement_record': {'state': Value('string'), 'scope': Value('string'), 'note': Value('string'), 'does_not_assert_current_public_fields': Value('bool')}, 'leader': Value('string'), 'accuracy': Value('float64'), 'interval': List(Value('float64')), 'separation_p': Value('float64'), 'separation_method': Value('string'), 'separation_sentence': Value('string'), 'separation_evidence': {'source': Value('string'), 'file': Value('string'), 'file_sha256': Value('string'), 'peritem_sha256': Value('string'), 'fleet': Value('string'), 'leader': {'model': Value('string'), 'k': Value('int64'), 'n': Value('int64'), 'accuracy': Value('float64'), 'wilson95': List(Value('float64'))}, 'next_best': {'model': Value('string'), 'k': Value('int64'), 'n': Value('int64'), 'accuracy': Value('float64'), 'wilson95': List(Value('float64'))}, 'paired_items': Value('int64'), 'discordant': {'leader_only_correct': Value('int64'), 'next_best_only_correct': Value('int64')}, 'mcnemar_p': Value('float64'), 'control': {'what': Value('string'), 'nperm': Value('int64'), 'seed': Value('int64'), 'fixed_pair_rate': Value('float64'), 'reselect_rate': Value('float64')}, 'recompute': Value('string')}, 'peritem_file_sha256': Value('string'), 'leader_source': Value('string'), 'leader_card_state': Value('string'), 'leader_card_note': Value('string'), 'note': Value('string'), 'distinct_items': Value('int64'), 'distinct_items_source': Value('string'), 'mde': {'value': Value('float64'), 'state': Value('string'), 'unit': Value('string'), 'alpha': Value('float64'), 'power': Value('float64'), 'test': Value('string'), 'paired_items': Value('int64'), 'discordance_rate': Value('float64'), 'source': Value('string')}, 'measurement_time': {'state': Value('string'), 'precision': Value('string'), 'observed_on': Value('timestamp[s]'), 'source': Value('string'), 'source_field': Value('string'), 'note': Value('string')}, 'dataset_url': Value('string')}
              because column names don't match

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Council of AI (CSOAI)

GSPC Academy — Openness (oss axis)

Training + MOOC data for the openness axis specialist — feeds the Council of AI agentic specialist hive and the GSPC Human-in-the-Loop academy.

Every item carries per-model outcomes from real measured runs: a 19-model fleet (6 base models — deepseek-r1:8b, gemma3:12b, llama3.2:3b, mistral:7b, qwen2.5:0.5b-instruct, qwen2.5:3b — and 13 CSOAI sov6-* prompt-overlay specialists), deterministic grading, 2026-08-12 measurement date, zero transport errors.

Stats

metric value
items 32
arms 19
hard cases (majority_wrong) 7
split 0
majority-correct 25
measurement rows behind it 608

The 7 majority_wrong items are the hard cases — the training gold for the openness specialist.

Per-arm accuracy (measured)

arm runs correct accuracy
deepseek-r1:8b 32 16 0.500
gemma3:12b 32 27 0.844
llama3.2:3b 32 14 0.438
mistral:7b 32 23 0.719
qwen2.5:0.5b-instruct 32 19 0.594
qwen2.5:3b 32 20 0.625
sov6-abstraction-v3-light 32 22 0.688
sov6-aesthetics-v3-light 32 27 0.844
sov6-agency-v3-light 32 27 0.844
sov6-creation-v3-light 32 27 0.844
sov6-destruction-v3-light 32 23 0.719
sov6-embodiment-v3-light 32 27 0.844
sov6-ethics-v3-light 32 20 0.625
sov6-identity-v3-light 32 22 0.688
sov6-logic-v3-light 32 21 0.656
sov6-preservation-v3-light 32 28 0.875
sov6-relationality-v3-light 32 20 0.625
sov6-synthesis-v3-light 32 16 0.500
sov6-temporality-v3-light 32 24 0.750

Fleet total: 423/608 correct (0.696).

Files

  • items.jsonl — one record per unique item: {item_id, scenario, gold_label, arms, discrimination, item_sha256}. Each arms entry is {prediction, correct} (plus runs/correct_runs where an arm was measured more than once on the item).

  • specialist-prompt.md — the lane's specialist prompt. Not yet statutory-pinned — honest placeholder, no rubric invented.

  • mooc-map.json — curriculum map: Observer (up to 30 easiest items), Steward (medium discrimination), Sentinel (the majority_wrong hard cases).

  • board-card.json — this axis's entry from the live GSPC board API, verbatim.

  • 30 arm outcomes were unparseable; they are recorded with prediction "UNPARSED" and correct: false — excluded nothing, hidden nothing.

Board row

bench task n separation fleet_mean dataset colour
OSSBench licence reasoning versus intended use 32 TIE 0.696 csoai/gspc-oss #2dd4bf

Live board: councilof.ai/board · live API serves the same axis entries (the board card here is a verbatim snapshot, measured_on behavioural axes 2026-08-12).

MOOC ranks

mooc-map.json assigns every item to exactly one tier: Observer (up to 30 easiest, largest correct-arm margin), Steward (medium discrimination: narrow majorities and split items), Sentinel (every majority_wrong hard case). Signing/review flows through the EAT review queue.

Upstream attribution & licence

Items are CSOAI's own GSPC bank for this axis (no external upstream). The raw per-item measurement rows are published at csoai/gspc-peritem-rows-2026-08-12.

Licence: CC-BY-4.0.

The rule

Measurement, not certification. Items are public; board changes require human-reviewed measurement runs.

Part of the GSPC Academy series — one dataset per measurement axis, patterned after the topic-partitioned JSONL + high-quality card format, with per-model measured outcomes on every item.
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