Bibliographic record
Abstract
Our journal is entering its ninth year of publication.Below are some statistics that show how far we have come:For the past nine years, we have received a total of 300 submissions, out of which 191 have been published.Our authorship is truly global, including authors from more than 30 countries, about half from the United States and Canada, but many are from developing countries including Nigeria, Pakistan, the Philippines, India, Tanzania, Ghana, Brazil, etc. Needless to say, our journal provides a platform to our peers who are underrepresented, and our publications offer a window into the current situation and perspective of libraries and information services around the world.Though we are not sure exactly where our readers come from due to the nature of online publication, our journal enjoys a large readership.Over the past nine years, the total views of abstract is 144,513 (835/per article) and the total full-text download is 73,584 (425/per article).Reflecting on the past nine years, we are proud of our journal's achievements, and we are grateful that we have a cohort of high caliber reviewers, copy editors, and most importantly, a worldwide authorship and readership.We are looking forward to even greater success!Now it is time to present the current issue (v.9, no.1) to our readers.This issue contains a variety of topics.And the authors are from five nations that are spread out across three continents: Asia, North America, and Africa.
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 | 0.018 | 0.067 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.025 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.020 | 0.021 |
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".