VICTORIOUS: A Visual Analytics System for Scoping Review of Document Sets
Bibliographic record
Abstract
Scoping review is an iterative knowledge synthesis methodology concerned with broad questions about the nature of a research subject. The increasingly large number of published documents in scholarly domains poses challenges in conducting scoping reviews. Despite attempts to address these challenges, the specific step of sensemaking in the context of scoping reviews is seldom addressed. We address sensemaking of a curated document collection by developing a VIsual analytiCs sysTem for scOping RevIew of dOcUment Sets (VICTORIOUS). Using known methods within the machine learning community, we propose and develop six modules within VICTORIOUS: Map, Summary, Skim, SemJump, BiblioNetwork, and Compare. To demonstrate the utility of VICTORIOUS, we describe three usage scenarios. We conclude by a qualitative comparison of VICTORIOUS and other available systems. While existing systems leave their users with singular information items regarding a document set and gaining an aggregated assessment in a scoping review is often a challenge, VICTORIOUS shows promise for making sense of documents in a scoping review process.
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 | 0.035 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.020 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.059 | 0.015 |
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".