Update methods_draft.md

methods_draft.md

@@ -1,6 +1,5 @@
 # Methods Document
 
-```markdown
 # Concept Atlas: Methods & Application Guide
 ### German Curriculum Semantic Analysis — Technical & Pedagogical Documentation
 
@@ -98,7 +97,7 @@ Each row in `curriculum_excerpts.csv` represents one curriculum excerpt:
 
 Before analysis, the corpus undergoes the following cleaning steps:
 
-```
+
 Raw CSV
   → Normalise column names (lowercase, underscores)
   → Fill missing values with empty strings
@@ -106,7 +105,7 @@ Raw CSV
   → Strip whitespace from text_excerpt and search_term
   → Remove excerpts shorter than 20 characters
   → Derive search_term_lower for case-insensitive concept matching
-```
+
 
 Concept subsets are built by **exact match** on `search_term_lower`, with
 automatic fallback to **partial string match** if fewer than 10 exact matches
@@ -129,7 +128,7 @@ The full analysis pipeline runs sequentially in a single click. All
 computationally expensive steps are cached to disk so that subsequent
 exploration is instantaneous.
 
-```
+
 CSV ingestion
       │
       ▼
@@ -163,7 +162,7 @@ Enriched parquet export
       │
       └──► data/enriched_corpus.csv
            cache/enriched_corpus.parquet
-```
+
 
 ### Caching strategy
 
@@ -281,7 +280,7 @@ Traditional methods (e.g. LDA) work on word co-occurrence statistics.
 
 ### Pipeline within BERTopic
 
-```
+
 Sentence embeddings (768-D)
         │
         ▼
@@ -297,7 +296,7 @@ Sentence embeddings (768-D)
         │
         ▼
   Topic labels + per-document probabilities
-```
+
 
 ### HDBSCAN: density-based clustering
 
@@ -594,7 +593,6 @@ automatically invalidated (different key → different filename → recomputed).
 
 ### Pushing to HuggingFace
 
-```bash
 # Authenticate
 huggingface-cli login
 
@@ -604,14 +602,14 @@ huggingface-cli upload deirdosh/curriculum_analysis_german \
 
 huggingface-cli upload deirdosh/curriculum_analysis_german \
   ./data data --repo-type=space
-```
+
 
 The `enriched_corpus.csv` adds BERTopic `topic_id` and UMAP coordinates
 (`umap2_x`, `umap2_y`, `umap3_x`, `umap3_y`, `umap3_z`) to every excerpt,
 making the enriched dataset independently useful for downstream analysis
 without re-running the pipeline.
 
----
+
 
 ## 12. Educational Applications
 
@@ -739,7 +737,6 @@ in the same CSV format enables direct cross-national comparison.
 
 To run the pipeline locally or contribute new analysis:
 
-```bash
 # Clone the Space
 git clone https://huggingface.co/spaces/deirdosh/curriculum_analysis_german
 cd curriculum_analysis_german
@@ -749,7 +746,7 @@ pip install -r requirements.txt
 
 # Run locally
 python app.py
-```
+
 
 A standard laptop (8 GB RAM, no GPU) can run the full pipeline in
 approximately 15–20 minutes on first run. GPU acceleration reduces this to