README.md

# temporal-context-gap-e1

Code and results for **E1: temporal-provenance audit** and **E2: era-classification
probe** — the first two empirical experiments of the position paper *"The Temporal
Context Gap: What Meaning Is Lost Between a Corpus and Its Consumer"* (draft,
September 2026, §6).

## E1 result (measured 2026-09-11, job 6aa330505527934177ec3185)

Temporal metadata is **fully present and fully crawl-time** in every corpus audited:

| Stratum | Docs sampled | % with temporal field | Distinct years | Years seen |
|---|---:|---:|---:|---|
| c4/en | 900,000 | 100.0% | 1 | 2019 only |
| c4/realnewslike | 80,858 | 100.0% | 1 | 2019 only |
| c4/en.noclean | 445,231 | 100.0% | 1 | 2019 only |
| fineweb/CC-MAIN-2013-20 | 900,000 | 100.0% | 1 | 2013 only |
| fineweb/CC-MAIN-2016-18 | 900,000 | 100.0% | 1 | 2016 only |
| fineweb/CC-MAIN-2019-18 | 900,000 | 100.0% | 1 | 2019 only |
| fineweb/CC-MAIN-2022-21 | 900,000 | 100.0% | 1 | 2022 only |
| fineweb/CC-MAIN-2025-26 | 900,000 | 100.0% | 1 | 2025 only |
| dolma3/common_crawl-{adult_content,art_and_design,crime_and_law} | ~67k each | 0% top-level; per-record `metadata.warc_date` confirmed | varies | 2015–2020 |

**Finding.** The headline was not "metadata is missing" but something stronger:
every temporal field that survives curation is a **fetch/crawl timestamp**
(C4's `timestamp` is the April-2019 crawl snapshot for all 1.43M sampled docs;
FineWeb's `date` is the WARC fetch date of its dump — a single year per
snapshot config; Dolma3's is `metadata.warc_date`, the per-record WARC fetch
time, nested under `metadata`). **The consumer side of the temporal context gap
survives curation; the production side — the authorship `time` a temporal key
needs (paper §5.2) — is absent everywhere.** A model trained on these corpora
can in principle know *when it fetched a document* but not *when it was
written*, except through self-anchoring expressions inside the text itself
(paper §4, Level 1 — measurable with `temporal_keys.selfanchor_density`).

Notes and caveats:
- c4/realnewslike and c4/en.noclean sampled fewer docs than c4/en because those
  configs' shard files contain fewer records per shard (3 shards × ~80–150k), not
  because of sampling failures.
- FineWeb's `date` column name suggests document dates; it is the crawl date
  (verified: within-week spread, one year per snapshot config).
- Dolma3's top-level schema is `{id, metadata, text}`; `created` (Dolma v1) is gone.
  The E1 audit script's `created`-top-level check returned 0% — the corrected
  measurement is the nested `metadata.warc_date`, verified in job
  6aa343175527934177ec364f.

## E2 result (measured 2026-09-11, job 6aa3644e5527934177ec410b)

Era-classification probe: TF-IDF + logistic regression predicting dump year from
raw text (first 4,000 chars, 100k features), FineWeb `sample-10BT` file 0,
15,000 docs per year cap. The sampled file contained 8 dump years
(2013, 2015–2020, 2022); 120,000 docs, 24,000-doc test set.

- **Year-from-text accuracy: 22.83%** vs. 12.5% majority baseline (1.8×, 8 balanced classes)
- **Within ±1 year: 41.35%; within ±2 years: 61.8%; mean abs year error: 2.27**
- Per-year accuracy flat (0.151–0.269; lowest 2017/2018)
- **Month probe not implementable on FineWeb**: each dump's fetch dates concentrate
  in the crawl window, so the corpus provides no fine-granularity time labels —
  itself a gap datum (consumer-side metadata cannot even define authorship-month labels)
- **Self-anchor density by year** (`temporal_keys.selfanchor_density`):
  declines monotonically, 0.588 (2013) → 0.415 (2022)
- **E1b — C4 URL-year heuristic (100k docs): 19.37%** of URLs carry a path-encoded
  year. Histogram concentrates 2009–2019 (peak 3,092 in 2018), collapsing after 2019
  (40 at 2020) — a validation: a URL year cannot postdate the April-2019 C4 crawl.
  A small id-like tail (years ≤1900 / far-future) is regex noise.

**Reading.** Coarse era signal survives curation and is measurable in raw text
alone (±2-year precision at 62%); fine granularity is unrecoverable — the corpus
cannot even supply its labels. The declining self-anchor trend suggests the
Level-1 anchor (paper §4) is eroding in web text.

## Files

- `temporal_keys.py` — the TemporalKey dataclass, `<KEY ... />` serialization and
  parsing (round-trip), K2 key-dropout masking, SemEval-2020-Task-1-style LSC
  drift-data annotation, and self-anchoring density. Paper §5.2/§5.3 implemented.
- `test_temporal_keys.py` — unit checks (run `python test_temporal_keys.py`).
- `audit_temporal_provenance.py` — the E1 audit script
  (`--quick` for validation, `--files N --rows M` for deep sampling).
- `e2_era_probe.py` — the E2 + E1b probe script (year probe, month probe,
  self-anchor density, URL-year heuristic).
- `RESULTS.md` / `results.json` — E1 deep-audit results (written by the job).
- `E2_RESULTS.md` / `E2_results.json` — E2 + E1b results (written by the job).

## Reproduce

```bash
pip install pyarrow zstandard huggingface_hub
python audit_temporal_provenance.py --files 3 --rows 300000 --out results.json --md RESULTS.md
pip install scikit-learn pyarrow numpy huggingface_hub
python e2_era_probe.py --per-year 15000 --out E2_results.json --md E2_RESULTS.md
```

## Costs

- E1 deep audit: ~$1.13 (cpu-xl, 67.5 min actual)
- Dolma3 follow-up + sandbox validation: ~$0.01
- E2 probe: ~$0.004 (cpu-upgrade, 7 min actual vs 60m ceiling)