Bibliographic record
Abstract
Many of today's postgrads and postdocs—be they in the United States or in Europe—are interested in exploring the widest possible range of scientific career options, everything from positions in academia and industry to careers 'off-the- bench but too often, they don 't know where to start, and, at the same time, lack knowledgeable mentors. Moreover, the pace of change within the scientific profession is accelerating. Suddenly, the benefits of multi-disciplinary career paths, mobility, and global collaborations outweigh their costs in complexity. Many university systems are coming to recognize this but are struggling to reform their traditional systems. And the pace of these changes threatens to outstrip the ability of administrators and faculty to keep current with new needs. In this environment, Science's Next Wave provides a broad array of novel Services while achieving economies of scale for subscribing institutions. Each week, Next Wave publishes online original articles providing career advice to young scientists and those who would mentor them. Över its 6 years of existence, the Next Wave website has developed an unmatched, living archive of information on career planning, job hunting, grant-writing, education reform, and much more. And it has become increasingly global, going beyond its starting point in the U.S. to develop collaborations with pan- European organizations and national bodies. For example, it is partnering with China’s national organizations and has established homepages in the UK, Canada, and Germany. Next to join: the Netherlands and Singapore.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.085 | 0.026 |
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".