Researcher engagement in policy deemed societally beneficial yet unrewarded
Bibliographic record
Abstract
Public support for research depends, in part, on the eventual societal benefits from research. Maintaining that support likely requires sustained engagement between the research community and the broader public. Yet, there is little organized effort to evaluate and reward such engagement in addition to research and teaching activities. Using data from an international survey of 1092 researchers (634 established researchers and 458 students) in 55 countries and 315 research institutions, we find that institutional recognition of engagement activities is perceived as being undervalued relative to its societal benefit. Many researchers report that their institutions would not reward engagement activities despite mission statements promoting engagement. Further, those institutions that actually measure engagement activities are perceived to do so in a limited capacity (respondents perceived that on average, 2 of the 7 dimensions of engagement we considered were reflected in evaluations). Most researchers are strongly motivated to engage for selfless reasons, which suggests that strong self-oriented incentives may have unintended effects. Perhaps by recognizing the important engagement activities of researchers, institutions can better achieve their institutional missions and bolster the crucial contributions of researchers to society.
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 | 0.133 | 0.251 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.028 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".