Bibliographic record
Abstract
Most people can agree that libraries are public goods, built upon ideals of egalitarianism and the democratization of information. But can we say that libraries exist without biases? LIS has been unpacking the issue of diversity for decades, particularly longstanding racial and ethnic biases, while simultaneously trying to shift the focus of diversity issues to include the full spectrum of human identity. This paper takes up the issue of racial and ethnic diversity in LIS, as two single components of the larger issue of diversity, in order to explore the dynamics of race and ethnicity amongst librarians themselves. La plupart des gens admettent que les bibliothèques sont des biens publics, construites sur les idéaux de l’égalitarisme et de la démocratisation d’information. Mais peut-on dire que les bibliothèques existent sans partialité? La science de l’information et des bibliothèques (SIB) cherche à éclairer le problème de diversité pendant des décennies, en particulier les partialités ethniques et raciales de longue date, tout en essayant de recentrer l’orientation des questions de diversité pour inclure tout l’éventail de l’identité humaine. Cette dissertation aborde la question de diversité dans les SIB, comme deux seuls composants de la question plus vaste de diversité, afin d’explorer les dynamiques de race et d’ethnie parmi des bibliothécaires eux-mêmes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.049 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".