Bibliographic record
Abstract
In this article, we argue that, just as an edition of a book can be a means of reifying a theory about how books should be edited, so can the creation of an experimental digital prototype be understood as conveying an argument about designing interfaces. Building on this premise, we explore theoretical affinities shared by recent design and book history scholarship, and connect those theories to the emerging practice of peer-reviewing digital objects in scholarly contexts. We suggest a checklist for subjecting prototypes directly to peer review: Is the argument reified by the prototype contestable, defensible, and substantive? Does the prototype have a recognizable position in the context of similar work, either in terms of concept or affordances? Is the prototype part of a series of prototypes with an identifiable trajectory? Does the prototype address possible objections? Is the prototype itself an original contribution to knowledge? We also outline some implications for funding agencies interested in supporting researchers who are designing experimental computer prototypes. For instance, if a series of prototypes functions as a set of smaller arguments within a larger debate, it might be more appropriate to fund the sequence rather than treating each project as an individual proposal.
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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.016 | 0.034 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".