kai-li-1994/garment-disruptor-rule-analysis: Documentation update with Zenodo DOI badge

Zenodo (CERN European Organization for Nuclear Research)

Abstract

v1.0.0 - Garment disruptor rule analysis workflow This is the initial archived release of the garment disruptor rule analysis workflow used for the manuscript: Embedded garment features limit textile sorting and fibre-to-fibre recycling in fast fashion Contents This release includes the Python scripts and derived output tables used to evaluate garment-level disruptor indicators from a component-normalized garment-variant dataset. Included materials: scripts/01_evaluate_disruptor_rules.py scripts/02_generate_figures.py outputs/disruptor_rule_flags_by_variant.csv outputs/disruptor_rule_summary.csv outputs/disruptor_aggregate_summary.csv outputs/disruptor_match_evidence.csv outputs/disruptor_trigger_diagnostics.csv outputs/disruptor_category_diagnostics.csv outputs/disruptor_regex_inventory.csv outputs/hardware_material_disclosure_summary.csv outputs/disruptor_summary_readable.txt README.md requirements.txt LICENSE CITATION.cff Analysis scope The workflow applies rule-based disruptor indicators to the companion component-normalized garment-variant dataset. It produces row-level flags, summary statistics, match-evidence records, trigger diagnostics, category diagnostics, regex inventories, and hardware material-disclosure summaries. The rule framework distinguishes: removable hardware-related disruptor indicators; retained structural and surface barriers; material-mismatch diagnostics for hidden layers and secondary components. Input dataset The input file required to rerun the workflow is: 6_JSONL_component_normalized.jsonl This file is not duplicated in this repository. It is archived separately in the companion dataset release: Dataset repository: https://github.com/kai-li-1994/garment-variant-dataset Dataset DOI: https://doi.org/10.5281/zenodo.20006389 Reproducibility To rerun the workflow: pip install -r requirements.txt python scripts/01_evaluate_disruptor_rules.py python scripts/02_generate_figures.py The output tables provide the tabular basis for the reported manuscript percentages, top-trigger rankings, category-level diagnostics, material-mismatch diagnostics, and audit checks. Notes The outputs identify observable disruptor indicators from retailer web data. They should not be interpreted as physical teardown results or direct recycling-process outcomes. R1–R3 are cue-based hardware indicators, while R4–R8 capture retained-barrier indicators. R7b and R8b are diagnostic material-mismatch indicators and are reported separately from the core retained-barrier aggregate.

Authors 1

1. Kai Li corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH