Bibliographic record
Abstract
This article posits a framework for the principles and practices of trans-inclusive bibliography, describing its necessity and challenges, considering historical analogues, and offering solutions for trans-inclusive bibliography in digital contexts. Proper names are the primary way that a person is credited with their scholarly labor, yet using names raises ethical issues. Trans-inclusive bibliography shares many of the same ethical concerns and strategies as trans-inclusive citation practices, but differs in terms of scale and stakes. Given that exhaustive enumerative bibliographies are often used to create individual works cited lists, their choices and practices can reverberate through an entire academic field. This article centers on enumerative bibliography, particularly the challenges and potential solutions for the World Shakespeare Bibliography. Trans-inclusive bibliography requires accepting bibliography's flexibility and contingency, relinquishing comforting myths about the stability of the historical record. We outline the ethical core of trans-inclusive bibliography and offer practicable scholarly habits to implement it.
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 | 0.048 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".