Toward a woven literature: Open-source infrastructure for networked scientific publishing
Bibliographic record
Abstract
Scientific publishing must evolve to reflect the computational nature of modern research. While code, data, and analysis increasingly shape scientific findings, current publishing formats often flatten these elements into static narratives. In response, a new model is emerging: the woven literature. Inspired by principles of literate programming, this approach integrates code, data, narrative, and interactive environments to enable reproducibility and transparency within the article itself. This review explores the technical foundations, cultural barriers, and infrastructural innovations shaping this transition. Drawing on case studies from the NeuroLibre platform, it illustrates how open-source tools can support next-generation publications that are executable, sustainable, and adaptable across disciplines. The discussion highlights how workflows of varying complexity can be modularized and surfaced through reproducibility-focused platforms, while also addressing limitations imposed by current incentive structures and legacy systems. By embedding computation directly within the scholarly article, woven literature transforms the role of publishing from passive documentation into an active and verifiable part of the research process, allowing readers to engage with and extend the work itself.
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 | 0.086 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.040 | 0.070 |
| Open science | 0.009 | 0.041 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.012 |
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".