The Forensic A-Index: A New Citation Algorithm for Restoring Integrity in Scientific Metrics
Bibliographic record
Abstract
This white paper introduces the Forensic A-Index, a novel bibliometric framework designed to address the global crisis of metric inflation, citation cartels, and industrialized paper mills. Modern science is drowning in its own metrics. Traditional indicators like the h-index systematically reward output volume and superficial visibility, creating a structural vulnerability that adversarial actors exploit to artificially inflate their perceived scholarly impact. To address this crisis, the A-Index transitions academic evaluation from raw, vulnerable accumulation to verified, topology-aware impact. Grounded in three foundational pillars - Integrity over Infinity, Fractional Justice, and Verified Expertise - the framework evaluates a researcher's portfolio through an active verification engine. It applies an L1-normalized fractional authorship attribution (the 10:1 Symmetry Protocol) to strictly conserve academic credit, integrates semantic citation weighting to penalize incidental padding, and enforces a dynamic network density penalty (Ktopo) to neutralize hyper-collaboration. By algorithmically deflating manufactured noise and artificial academic capital, the A-Index fundamentally defends scientific meritocracy, ensuring that academic credit flows exclusively to authentic innovators who possess verifiable conceptual understanding. Note: This document represents Part 1 of the foundational ASAI architecture, focusing on the core algorithmic philosophy and practical application. For the rigorous game-theoretic proofs and stochastic diffusion models, please refer to Part 2: "The Science of Truth: A Rigorous Mathematical Framework for Integrity-Aware Scientific Metrics via the Forensic A-Index." *** To test the live algorithmic verification engine, visit appliedsciai.com.
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 | 0.007 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".