Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research paper undertakes a comparative analysis of the Goods and Services Tax (GST) frameworks of Australia, Canada, and the European Union (EU) to identify actionable insights for improving India’s existing GST system. While India’s GST regime represents a landmark shift towards indirect tax unification, it continues to face challenges including multi-rate complexity, compliance burdens, and inter-governmental fiscal disputes. In contrast, Australia’s single-rate, digitally integrated GST model, Canada’s dual-tier federal–provincial structure, and the EU’s harmonized yet flexible VAT system offer diverse lessons in simplicity, administrative coordination, and cross-border taxation. The study analyses structural features such as rate design, compliance systems, revenue distribution models, and taxpayer experience. Through doctrinal and comparative legal methodology, it evaluates the effectiveness, efficiency, and equity of each framework. The findings suggest that adopting simplified tax structures, enhanced digital infrastructure, and more transparent inter-governmental coordination could substantially improve India’s GST architecture. The research contributes to the discourse on tax reform by offering policy recommendations grounded in international best practices tailored to India’s federal context.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it