Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
2Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2154709503
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Avijit Roy (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Inequality: 1 paper
- Skills Training: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Tokenizers charge some languages more: Bengali debugging text consumes roughly 1.56× GPT-4o tokens (and up to 4.5× on open-weight tokenizers), shrinking effective context windows and raising API or local-deployment costs for learners; Yoruba incurs even larger GPT-4o fragmentation despite using Latin script, showing script alone does not explain the penalty.arxiv | Avijit Roy provider id |
2026-08-10 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.