Preterm Birth and Healthy Outcomes Team: the science and strategy of team-based investigation
Bibliographic record
Abstract
In the traditional academic environment, there are often more reasons not to construct a team than there are reasons to construct one. In particular, there are institutional and funder guidelines for reward and recognition that are disincentives to the creation of a team. There are temporal pressures that favor small group work and incremental science over large groups and high risk projects. In addition, we are increasingly encouraged to make our research relevant and valid while reducing the time required for the translation of evidence to practice and policy. While the need for accurate science is paramount, these requests must still be accommodated within the parameters of academic and health delivery systems in flux, reductions in research budgets, and changing government priorities.
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 | 0.318 | 0.292 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.031 | 0.020 |
| Open science | 0.006 | 0.039 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".