performing-selective-coding

Selective coding integrates grounded theory categories into a coherent core category, storyline, and theoretical framework by processing category and relationship JSON artifacts to construct propositions, mechanisms, and boundaries.

Theoretical saturation checking evaluates saturation across new concept rate, category completeness, relationship stability, and theory completeness, producing a standardized saturation JSON report to confirm if your grounded theory model is saturated.

Storyline construction builds a narrative by deriving a timeline, selecting key events, identifying actors, and assembling a causal chain aligned with the core category from your category and relationship JSON files.

Yes, the selective coding workflow uses networkx and scikit-learn to compute core category metrics, evaluating explanatory power, connectivity, and data support to validate the most plausible core category.

You need categories.json, relationships.json, and new_data/data files as inputs to execute the selective coding toolchain and produce core_category.json, storyline.json, theory.json, and saturation.json outputs.

Yes, theoretical saturation inspection requires new_data/data files to evaluate the new concept rate and relationship stability, confirming that your theoretical framework achieves category and theory completeness.