Bibliographic record
Abstract
The scholarship of integration is concerned with making connections across scientific disciplines, placing the work of individual investigators and their specialty fields into a larger context, and educating nonspecialists. The authors focus their comments on the biomedical sciences, but observe that closer integration of the biomedical and behavioral sciences will be particularly crucial to advance understanding of the human brain. They observe that as biomedical sciences become more technologically sophisticated, progress is increasingly dependent on sciences such as physics, chemistry, engineering, and related fields. However, the scholarship of integration has been slower than other forms of scholarship to gain acceptance as an integral activity of the professoriate. The isolation of disciplines from one another, particularly at large universities, and the perception of interdisciplinary work as risky and professionally unrewarding are among the forces that may discourage integrative scholarship. In addition, a troubling disconnect exists between the scientific community and the larger public in the understanding of science. Leaders in academic medicine and science must develop strategies to move interdisciplinary work from the margins into the mainstream of academia. Solutions that have been proposed include creating new research entities and funding mechanisms dedicated to interdisciplinary work; reinvigorating the integrative role of the physician-scientist; and training specialists in translational research. The scientific community must also work to develop more effective means of communicating the importance of its work to the public.
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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.020 | 0.052 |
| Scholarly communication | 0.033 | 0.052 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.013 | 0.030 |
| Insufficient payload (model declined to judge) | 0.037 | 0.013 |
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".