add E2 era-probe and E1b URL-heuristic results

README.md

@@ -1,10 +1,11 @@
 # temporal-context-gap-e1
 
-Code and results for **E1: temporal-provenance audit** — the first empirical
-experiment of the position paper *"The Temporal Context Gap: What Meaning Is Lost
-Between a Corpus and Its Consumer"* (draft, September 2026, §6 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).
 
-## Result (measured 2026-09-11, job 6aa330505527934177ec3185)
+## E1 result (measured 2026-09-11, job 6aa330505527934177ec3185)
 
 Temporal metadata is **fully present and fully crawl-time** in every corpus audited:
 
@@ -43,6 +44,31 @@ Notes and caveats:
   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
@@ -51,11 +77,22 @@ Notes and caveats:
 - `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).
-- `RESULTS.md` / `results.json` — deep-audit results (written by the job).
+- `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)