Re-Invigorating Hibar Research for The 21St Century: Enhancing Fundamental Research Excellence in Service to Society
Bibliographic record
Abstract
More than ever, society needs research breakthroughs to address major problems. Universities have a key role to play in discovering the required new knowledge and guiding its application. However, since World War II, universities have been encouraged to focus mainly on curiosity-based research, with corporations carrying out practical work. This division worked well in the last half of the 20th century, when there was considerable funding for long-term research in the laboratories of major corporations. Today, however, those firms face greater competition, and the resultant financial constraints have foreshortened their research time-horizons. Universities are poised to compensate by re-emphasizing long-term, application-oriented research, but great care must be taken to strengthen fundamental research as well. These objectives can be achieved simultaneously by bolstering a time-honored class of research projects labelled Highly Integrative Basic And Responsive (HIBAR), each of which combines fundamental and applied approaches through partnerships with practical experts. This approach will help replicate, within universities, the breakthrough-generation capacity that once flourished in major corporate laboratories. Toward this end, a network of universities called the HIBAR Research Alliance (HRA) has recently formed to strengthen HIBAR research, both by helping universities to encourage it (while also improving equity, diversity, inclusion, and academic freedom) and by helping researchers to carry out HIBAR research projects (while also advancing their careers). The HRA aims to increase the rate of HIBAR research projects in universities—from about one project in 20 today to one in five by 2030—while strengthening all types of research excellence.
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 | 0.063 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.027 | 0.033 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".