Data and code for: "Low-Resource Topic Modeling for Domain Experts: A Pilot Study in CO₂ Mineralization Literature"

This repository contains the data and analysis code accompanying the manuscript "Low-Resource Topic Modeling for Domain Experts: A Pilot Study in CO₂ Mineralization Literature" (Sabatino et al.). The study benchmarks four topic-modeling pipelines (MalletLDA, ProdLDA, BERTopic+SciBERT, SciBERT-NVCTM) implemented on consumer hardware (Mac Mini M2, 16 GB RAM) by domain experts without formal NLP training, on a corpus of 286 CO₂-mineralization papers (2013–2025). It also includes a four-level zero-shot ablation of a Large Language Model (Claude Haiku 4.5) for innovation detection, a calibrated outlier-detection ensemble, and a semantic bridge-term network analysis. Contents data/metrics/ — per-seed model metrics, aggregated metrics, ANOVA + Bonferroni results, with regeneration script. data/stability/ — cross-seed stability (ARI, Jaccard, doc- and topic-word-level). data/outlier_ensemble/ — ensemble outlier scores for all documents and the 9 declared outliers. data/llm_ablation/ — per-paper R1–R4 decisions, ablation table, run summaries, pick-level overlaps. data/topic_assignments/ — top-1 topic assignments per model. data/figure2_macroclusters/ — topic→cluster mapping, per-period weights, Jaccard matrices, figure-regeneration scripts. code/ — full pipeline: LLM baseline (with verbatim prompts and raw outputs), outlier detection, topic metrics, topic modeling, and semantic network analysis (see the NOTA_provenienza.md / README.md files in each subfolder for provenance). All Claude Haiku 4.5 calls used the model snapshot claude-haiku-4-5-20251001. The analysis was produced on consumer hardware without fine-tuning, retrieval augmentation, or specialized infrastructure. Related / alternate identifiers To add after acceptance: the journal article DOI, relation "is supplemented by this upload" (or "is supplement to"). Cite the concept DOI (the "all versions" DOI) in the manuscript's Data Availability Statement, so it always resolves to the latest version.