docs(readme): align with canonical source-of-truth (cond-4 framing, cond-3 vs cond-4 distinction)

README.md

@@ -59,9 +59,20 @@ dataset_info:
 
 # Language Decoded — Community Code
 
-Natively-authored multilingual code for the **Language Decoded** project (part of [Cohere's Tiny Aya Expedition](https://aya.for.ai)). This dataset contains code written by developers in non-English programming languages and code with significant CJK content — **not** mechanically transpiled from English.
+Natively-authored multilingual code for the **Language Decoded** project (part of [Cohere's Tiny Aya Expedition](https://aya.for.ai)). This dataset contains code written by developers in non-English programming languages and code with significant CJK content — **not** mechanically transpiled or LLM-translated from English.
 
-This data serves as a component of **Condition 3** ("Mixed Native Sources") and **Condition 4** ("Strictly Native Code") in the Language Decoded experiment, which tests whether native-language code improves multilingual reasoning beyond keyword swapping alone.
+> **Experiment and proposed paper title:** _Language Decoded: Exploring the Impact of Native Code on Multilingual Models_
+
+This data serves as the corpus for **Condition 3** ("Mixed Native Sources") and is intended to serve as the corpus for **Condition 4** ("Community-Contributed Native Code") in the Language Decoded experimental ladder. See [legesher/language-decoded-experiments](https://huggingface.co/datasets/legesher/language-decoded-experiments) for the canonical project description.
+
+## How Condition 3 and Condition 4 differ
+
+Both conditions deal with native-language code, but they ask different questions:
+
+- **Condition 3 ("Mixed Native Sources")** uses code pulled from real-world public-source repositories — incidentally available code that humans wrote in or with the target language. Phase 3 trained `condition-3-zh-5k` from data assembled here.
+- **Condition 4 ("Community-Contributed Native Code")**'s design goal is **code whose problem-solving logic is itself native** — written as if a native speaker were approaching the problem, not English code that was later translated. Currently pending sufficient direct community contributions to assemble a stable training corpus; in neither Phase 2 nor Phase 3 evaluation. Cond-5's fully-translated data served as Phase 3's practical proxy because gathering native-authored code at scale proved difficult.
+
+If you'd like to contribute Python (or other-language) code where you approached the problem in your native target language, the contribution interface is the [`legesher/legesher-native-code`](https://huggingface.co/spaces/legesher/legesher-native-code) HF Space — contributions there feed into the cond-4 corpus.
 
 ## Available Configs
 
@@ -69,6 +80,8 @@ This data serves as a component of **Condition 3** ("Mixed Native Sources") and
 | ------ | -------- | ----- | --------------------------------------------- |
 | `zh`   | Chinese  | 3,486 | Natively Chinese-authored code from 5 sources |
 
+Native code for Spanish and Urdu is not yet available.
+
 ## Schema
 
 | Column         | Type   | Description                                           |
@@ -133,22 +146,25 @@ high_quality = train.filter(lambda x: x["quality_tier"] in ("A", "B"))
 
 ## Relationship to Other Datasets
 
-- **[legesher/language-decoded-data](https://huggingface.co/datasets/legesher/language-decoded-data)**: The main experiment dataset with transpiled code (conditions 1–2), blended datasets (condition 3), and strictly native code (condition 4). Conditions 3 and 4 use native code from this repo.
-- This repo stores the **raw native code** with full metadata. The blended and native training datasets live in `language-decoded-data`.
+- **[legesher/language-decoded-data](https://huggingface.co/datasets/legesher/language-decoded-data)**: The main training data hub. Holds the per-condition training corpora (cond-1 raw English, cond-2 Legesher-transpiled, cond-3 mixed native sources, cond-4 native-authored, cond-5 fully translated via `c4ai-aya-expanse-32b`).
+- **[legesher/language-decoded-experiments](https://huggingface.co/datasets/legesher/language-decoded-experiments)**: The canonical project source-of-truth — experiment tracking, evaluation results, analysis, and the full experimental ladder.
+- **[legesher/language-decoded-lora](https://huggingface.co/legesher/language-decoded-lora)**: LoRA adapters trained on the per-condition corpora.
+- This repo stores the **raw native code** with full metadata. The blended and native training datasets used for fine-tuning live in `language-decoded-data`.
 
 ## Limitations
 
 - **Chinese only**: Currently limited to Chinese-language code. Native code for Spanish and Urdu is not yet available.
 - **License uncertainty**: Some files (particularly from `thestack`) have `UNKNOWN` licenses. These were included because they appeared in The Stack's permissive-license subset, but individual file licenses could not always be verified.
 - **Quality variation**: Quality tiers are assigned heuristically based on CJK content ratio, file size, and structural indicators. Tier D files may contain minimal native-language content.
-- **Non-Python files included**: Unlike the transpiled datasets (conditions 1–2), this dataset includes code in multiple programming languages (Python, Java, JavaScript, Wenyan, Qi, Mulan, etc.), reflecting the reality of native-language programming ecosystems.
+- **Non-Python files included**: Unlike the Phase 3 training corpora for cond-1, cond-2, and cond-5 — which are Python-only — this dataset includes code in multiple programming languages (Python, Java, JavaScript, Wenyan, Qi, Mulan, etc.). This reflects the reality of native-language programming ecosystems and is intentional for cond-3.
 - **CJK-heavy bias**: Files were selected partly based on CJK character presence, which may over-represent code with Chinese comments/strings rather than code with Chinese-language syntax.
+- **Native-authored ≠ scraped**: Although this corpus comes closer to native-authored code than the transpiled (cond-2) or fully-translated (cond-5) corpora, the inclusion criteria are based on CJK presence and source provenance, not on whether the original author was thinking in the target language while writing the code. Cond-4 will eventually distinguish that more cleanly via direct contribution.
 
 ## Citation
 
 ```bibtex
 @misc{language-decoded-2026,
-  title={Language Decoded: Investigating Language-Dependent vs. Structure-Dependent Reasoning Benefits of Code},
+  title={Language Decoded: Exploring the Impact of Native Code on Multilingual Models},
   author={Madison Edgar and Saad Ahmed Bazaz and Tom Sherborne and Rashik Shahjahan and Khojasteh Mirza and Sarah Jawaid and Rafay Mustafa and Sohaib Ahmed Bazaz},
   year={2026},
   publisher={Hugging Face},
@@ -158,4 +174,4 @@ high_quality = train.filter(lambda x: x["quality_tier"] in ("A", "B"))
 
 ## License
 
-Apache 2.0
+Apache 2.0