Bibliographic record
Abstract
In this article I undertake an historical analysis exploring how civil society, government, industry, and research sectors have adopted and adapted various publishing technologies over time—including the printing press, typewriters, microfiche, photocopiers, computers, email, pdfs, websites, and databases—to communicate research and ideas. Shining a light on this shadow history reveals the way the centripetal and centrifugal forces of democratization, science, and commercialization have intersected with changing technologies to foster a diverse research publishing economy which features both centralized and decentralized trajectories. While scholarly academic publishing has moved from informal letter exchanges towards formalized standards of production, and eventually to the global business it is today, governments, civil society organisations, research institutions, and industry have continued to operate as small-scale, often ad hoc publishers, producing and distributing research and other publications for various purposes, using and adapting a range of technologies and business models, first in print and continuing in digital formats. The history of organization-based research publishing (grey literature) shows the ways in which a range of new media tools and technologies have, at any given time, been co-opted by groups for public influence and impact, and have continued in various informal and decentralized business models at the same time as other forms of scholarly communication have become increasingly aggregated.
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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.013 | 0.033 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".