Beyond advertising: New infrastructures for publishing integrated research objects
Bibliographic record
Abstract
Moving beyond static text and illustrations is a central challenge for scientific publishing in the 21st century.As early as 1995, Donoho and Buckheit paraphrased John Claerbout that "an article about [a] computational result is advertising, not scholarship.The actual scholarship is the full software environment, code and data, that produced the result" [1].Awareness of this problem has only grown over the last 25 years; nonetheless, scientific publishing infrastructures remain remarkably resistant to change [2].Even as these infrastructures have largely stagnated, the internet has ushered in a transition "from the wet lab to the web lab" [3].New expectations have emerged in this shift, but these expectations must play against the reality of currently available infrastructures and associated sociological pressures.Here, we compare current scientific publishing norms against those associated with online content more broadly, and we argue that meeting the "Claerbout challenge" of providing the full software environment, code, and data supporting a scientific result will require open infrastructure development to create environments for authoring, reviewing, and accessing interactive research objects.
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 | 0.083 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.038 | 0.076 |
| Open science | 0.010 | 0.026 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.023 | 0.014 |
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".