Bibliographic record
Abstract
This case study examines the viability of digital social platforms for library workers to connect as individuals and professionals. Inspired by the recent decline of Library Twitter, the once de-facto site of conversation due to its popularity and adoption among information institutions, we seek to address the ramifications of cultural and infrastructural changes brought on by the takeover of Elon Musk. Rebranding the site to X, the “everything website”, the tech billionaire has drastically changed site affordances, while pushing users away through predatory monetization, yet failing to address rising dis and misinformation. Through a combined methodology of literature review, platform analysis, and community discussion, and adapted from a poster presentation by the authors on the same topic, this paper explores the information behaviour and migration trends of Library Twitter users, offers an assessment of alternative platforms, and presents key considerations for the future of library community networks. Anxieties about where to go next and the increasing evidence that all platforms are susceptible to “enshittification” to create a valid sense of urgency, however, we offer this moment as a rare opportunity to build new digital spaces with care and intention.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.091 | 0.019 |
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".