Bibliographic record
Abstract
Almost a year ago, in the summer of 2023, the managing editors of the Emerging Library and Information Perspectives (ELIP) journal called upon LIS graduate students and recent alumni to submit their papers for the sixth edition of the journal. This marked the beginning of a significant transformation for ELIP, transitioning it from a structured process managed by a hired editor and faculty advisory board to a fully student-run, volunteer-driven journal. The relaunch involved revamping the editorial process, expanding the author pool, and enhancing outreach efforts, all aimed at creating a more inclusive and independent platform. The new issue exemplifies these changes, featuring diverse contributions across multiple categories, from research articles on TikTok's algorithms and information science ontologies to insightful discussions on podcasts and Wikipedia for information literacy. The managing editors, reflecting on the journey, now pass the baton to future editors, eager to see the continued growth and impact of ELIP within the LIS community.
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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 | 0.006 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.022 | 0.024 |
| Insufficient payload (model declined to judge) | 0.026 | 0.025 |
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".