Mapping the World of Library Publishing: Unveiling the Global Landscape and Collaboration behind the Scenes
Bibliographic record
Abstract
This article is derived from the presentation titled “Mapping the World of Library Publishing: Revealing the Global Landscape and Collaborative Efforts” delivered during the panel discussion “Working Together to Expand Library Publishing Globally” at the World Library and Information Congress (WLIC) 88th IFLA General Conference and Assembly on August 22, 2023, held in Rotterdam, The Netherlands. It provides insights into the background and collaborative dynamics among key stakeholders, including the IFLA Library Publishing Special Interest Group, the Library Publishing Coalition, and various international entities working behind the scenes of the Global Library Publishing Map project. The article delves into the project’s development, presenting a comprehensive analysis of all participating libraries and other organizations to unveil the global library publishing landscape as visualized on the Map. Moreover, it addresses the challenges encountered during the creation and maintenance of this Map. Finally, the paper explores the future directions and ongoing work in this important endeavor.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".