Becoming the First Open Access Journal in the <i>Macromolecular</i> Journals Family
Bibliographic record
Abstract
After the launch of a new logo and cover design, along with restructuring our editorial responsibilities last year, Macromolecular Materials and Engineering is not resting on its laurels, as we continue to improve, reiterate, and refine our journal strategies for our continued success!In this editorial you will read about Macromolecular Materials and Engineering's transition to Open Access, our new metrics in the Journal Citation Reports, a special issue, changes to the editorial board, and feedback for our reviewers. Open AccessAfter careful consideration of all options available for future development, we have taken the decision to convert Macromolecular Materials and Engineering to an open access journal.So, as of this issue, Macromolecular Materials and Engineering is only publishing open access articles.This has a number of advantages for readers and authors.While readers enjoy the convenience of being able to view every open access article without having to worry about library subscriptions, authors also benefit from increased visibility.Our data show that across Wiley journals, publishing open access results in more downloads, more citations, and greater visibility from the general public.Additionally, many funding agencies are beginning to require publicly funded research to be made available to the general public, and open access is an excellent option to meet these needs, increasing transparency and the likelihood of your work to have a strong impact on the progress of our society.You can find more information on the benefits of public access at the end of this editorial.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.034 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.039 | 0.032 |
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".