Supplementary Materials for: A data-driven analysis of structural asymmetries in the gTLD registrar market
Bibliographic record
Abstract
This repository contains the supplementary materials for the article "A Data-Driven Analysis of Structural Asymmetries in the gTLD Registrar Market," published in IEEE Access, vol. 14, pp. 103809–103819, 2026, doi: 10.1109/ACCESS.2026.3709387. It includes a modular Python pipeline to download, normalize, and merge ICANN-accredited registrar lists with monthly gTLD transaction CSVs (.com, .net, .org, .info, .shop, .store, .top, .xyz). The package provides raw and processed datasets (including registrar metadata and transaction summaries by TLD), documentation, and a complete requirements.txt to reproduce the empirical results of the article as corrected in the accompanying Correction (see below); the original Table 4 cannot be reproduced from this dataset, as documented in the README. Each step can be run individually or end-to-end via run_pipeline_optimized.py, which generates the final merged tables (merged_transactions.csv, analysis_dataset.csv) used in the paper. For full usage instructions and parameter details, please see the included README.md. Version 1.2 (September 2026) accompanies a Correction to the article submitted to IEEE Access. The pipeline scripts and all data files are unchanged from version 1.1; we repaired test_integration.py so the tests load the step modules instead of placeholder functions (20 of 20 tests pass). We added the list of eight technical IANA registrar IDs excluded in the Correction (excluded_iana_ids.csv), the verification script correction_metrics.py with its output correction_metrics.csv and run log, which recomputes every published figure for the original and corrected population, and the reconciliation script and log for Table 4. The README documents the known issues found during verification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.478 | 0.207 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".