Issue Information
Bibliographic record
Abstract
The Journal of Product Innovation Management is the leading academic journal devoted to the latest research, theory, and practice in innovation and new products (goods, services, and hybrid solutions) development.The scope of the journal is broad, taking account of issues that are crucial to successful innovation in the organization's external and internal environments.The intent is to be informative, thought-provoking and intellectually challenging thereby contributing to the knowledge and practice of new product development and innovation management.It is one of the important benefits of being a PDMA member, although subscriptions are also available directly from the publisher. Aims and ScopeThe Journal of Product Innovation Management is an interdisciplinary, international journal that seeks to advance our theoretical and managerial knowledge of product and service development.The journal publishes original articles on organizations of all sizes (start-ups, small to medium enterprises, large corporations) and from the consumer, business-to-business, and policy domains.The journal is receptive to all types of quantitative and qualitative methodologies.Authors across the world from diverse disciplines and functional perspectives are welcome to submit to the journal.Articles published in the journal consist of two types: • Original research articles including conceptual/theoretical and empirical articles.All research articles are subject to double-blind, refereed peer review.• "Catalyst" articles, which do not fit into the category of original research yet provide value to scholars and/ or practitioners.Such articles may, among others, add a unique commentary on an ongoing scholarly debate, revisit seminal articles and research streams with new insights, or highlight gaps in current research and opportunities for new streams based on some unique perspective (years of scholarly experience, extensive experience in the front lines of practice, and so on).
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.861 | 0.815 |
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".