The problem: 16 countries that no per-country model can learn

Voidly's classifier v3.3 works well on countries with many labels (Iran, Russia, China — all at F1 ≈ 1.0). But a quiet failure hides in the long tail: OM, UZ, TN, LY, YE, JO, MA and ten other MENA / former-Soviet states regress 5–29pp F1 compared to v3.1. We tried a regime-cluster fine-tune (v3.4) and it didn't help — documented in that finding.

The root cause: with fewer than 5 confirmed censorship incidents, there is no per-country signal for ANY parametric model to learn from. v3.3 effectively falls back to the global prior — a flat ~16.7% block rate — for every tail country. That is the wrong answer for Qatar (87%), Kuwait (76%), and Bahrain (68%) and the right answer for almost no one.

The fix: project countries into a shared embedding space

Instead of asking "what does this country's history say about itself?", we ask "which other countries does this one look like, and what do they say?" That's zero-shot cross-country transfer — fit a regressor from per-country meta-features → empirical block rate on the countries that DO have signal, then project every country (including the 16 tail ones) into that same feature space.

Meta-features we use (39 total after one-hot encoding):

Geographic: continent (one-hot), UN subregion (one-hot)
Regime: risk_tier (0–4), regime_type indicator (pervasive / targeted), baseline_block_rate
Historical (365–730 days ago, disjoint from label window): n_incidents_hist, n_severe_hist, mean_block_rate_hist, n_distinct_domains_hist, n_distinct_asns_hist, n_evidence_hist
Structural (last 90 days): n_probes_90d, n_unique_refs_90d

The label is the empirical block_rate over the past 365 days. Historical features sit in a window 365–730 days ago — disjoint from the label window — so the model can't trivially memorise.

Validation: leave-one-country-out cross-validation

We bake off Ridge (alpha 1.0), Ridge-strong (alpha 10), GradientBoosting (max_depth 2 / 3, n_estimators 100 / 200) under LOOCV. gbm_shallow (max_depth=2, n_estimators=100, lr=0.05) wins:

candidate	LOOCV R²
ridge (α=1)	0.245
ridge_strong (α=10)	0.313
gbm_shallow (depth 2)	0.487
gbm (depth 3)	0.468
gbm_wide (depth 3, 200 trees)	0.453

Tail country uplift vs v3.3's flat prior

v3.3 effectively predicts the global prior (~16.7%) for every tail country. Zero-shot uses meta-features. Direct comparison:

country	empirical	zero-shot pred	v3.3 prior	zero-shot abs err	v3.3 abs err	improvement
TN	0.66	0.49	0.17	0.17	0.49	+32pp
JO	0.50	0.56	0.17	0.06	0.33	+27pp
QA	0.88	0.44	0.17	0.44	0.71	+27pp
UZ	0.44	0.50	0.17	0.05	0.28	+22pp
SY	0.46	0.55	0.17	0.09	0.30	+20pp
LY	0.42	0.48	0.17	0.06	0.25	+19pp
OM	0.37	0.40	0.17	0.03	0.21	+18pp
MA	0.53	0.33	0.17	0.19	0.36	+16pp
YE	0.55	0.33	0.17	0.22	0.38	+16pp
BH	0.68	0.17	0.17	0.51	0.51	+0.2pp
TJ	0.04	0.21	0.17	0.17	0.13	−4pp
TM	0.30	0.12	0.17	0.18	0.13	−4pp
KW	0.76	0.09	0.17	0.68	0.60	−8pp
KG	0.34	0.06	0.17	0.28	0.18	−10pp
DZ	0.26	0.46	0.17	0.20	0.09	−11pp
IQ	0.20	0.51	0.17	0.31	0.04	−27pp

10 of 16 tail countries (62.5%) beat v3.3's flat prior in absolute error. Median tail improvement: +16pp.

Honest caveats

The model is country-LEVEL. It predicts a long-run block rate, not a day-level shutdown probability. Don't confuse it with v3.3's per-day censorship classifier.
Three Gulf states (BH, KW, QA) have empirical block rates of 68–88% — outliers compared to the training distribution. Zero-shot under-predicts them by 30–45pp. It still beats v3.3 on KW and QA in absolute terms; only Bahrain is a wash.
The 90% CI uses normal approximation on residual_std (≈ 0.155). For countries whose structural traits are far from anything in training, the CI may understate uncertainty.
An early v1 had target leakage through mean_block_rate_365d (the feature window overlapped the label window). LOOCV R² was 0.9997 — clearly suspicious. Fixed by moving features to a 365–730-day-ago window before promote.
This is a second-opinion signal. v3.3 is still authoritative for countries where v3.3 already performs well.

Promoted

Promote floor was set at: LOOCV R² ≥ 0.30 AND median tail improvement vs v3.3 ≥ 0pp AND ≥60% of tail beats v3.3 prior in absolute terms. All three are met (R² 0.487, median improvement +16.2pp, 10/16 = 62.5% beat). Live at GET /v1/forecast/zero-shot/<cc>.

Endpoints

GET /v1/forecast/zero-shot/<cc> — block-rate point estimate + 90% CI for any country.
GET /v1/forecast/zero-shot/info — full sidecar (candidates, LOOCV metrics, tail evaluation, honest caveats).
GET /v1/forecast/zero-shot/coverage — all 148 country predictions in one call.