Bibliographic record
Abstract
Microsoft Word's dominance as an authoring tool creates substantial inefficiencies in the scholarly authoring ecosystem. Many journals and journal management platforms are designed around uploading and downloading incrementally updated drafts of Word manuscripts, creating a difficult-to-manage ecosystem of individual change-tracked files and annotated PDFs. For most end users, there is no sufficiently easy to use or widely accepted alternative to this. Yet, when it comes to publishing, the scholarly publishing industry has (mostly) settled on a structured format—JATS XML. This disconnect between the tools and formats used for authoring and the formats required for publishing has meant that, for several decades now, manuscripts received from authors will need to be entirely XML-typeset by publishers at considerable expense. Texture is a WYSIWYG editor app that allows users to turn raw content into structured content, and add as much semantic information as needed for the production of scientific publications. The primary goal of Texture is to remove this requirement for XML expertise by providing a solution for publishers to bring accepted papers to production more efficiently. Texture reads and produces valid JATS files. This allows Texture to work seamlessly in existing publishing workflows. The Public Knowledge Project has continued to develop their Open Typesetting Stack (OTS) application for automatically transforming Word or PDF articles into JATS XML. We currently have an alpha plugin for integrating OTS into our Open Journal Systems publishing platform; this plugin includes Texture. Our solution, using the Open Typesetting Stack and Texture, aims to address the impracticalities of trying to "reverse-engineer" an author's work in Word while still supporting a polished, professional typesetting workflow.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.036 |
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".