Bibliographic record
Abstract
The Ins and Outs of the Peer-Review ProcessThis whole game of peer reviewing is new for some of us librarians.In academic libraries, it is presumed that librarians perform some kind of scholarly activity as part of their responsibilities and as a way to ensure continuing appointment, tenure or promotion.Scholarly activity and creative projects are recognized in various ways, and publishing in a peer reviewed paper can be an integral part of fast-tracking your career path.However, it can also be intimidating.The fact that the peer review process can be scary was part of the raison d'être for this journal.The first editorial board wanted a journal that felt approachable and connected to your local or regional library association, but they also knew that rigour and high quality papers were needed to make this publishing venture truly successful for everyone.I believe that we have done that.However, after reviewing some of the peer reviewing documents and communicating with a few of the section editors over the last three issues of the journal, I began to get a little worried and started to ask myself some questions.So, here are the questions and my answers."Are authors starting to feel dejected because their papers need substantial revision or have not been accepted the first time around?"
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 | 0.248 | 0.591 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.014 | 0.031 |
| Scholarly communication | 0.038 | 0.025 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".