Imagine moving behavioral science findings languishing in scholarly journals to public consumption
Bibliographic record
Abstract
Where has our reach and impact gone? As behavioral scientists, it is incumbent upon us to extend the reach of our work. Doing so is never easy unless you follow a few approaches. This manuscript underscores the imperative for behavioral scientists to communicate their research findings beyond traditional academic confines, targeting non-scientific audiences. We outline strategic steps that scholars can adopt to enhance the visibility, accessibility, and impact of their research. These include (1) translating scientific jargon into comprehensible language; (2) leveraging digital platforms like blogs, podcasts, and social media; (3) collaborating with media professionals for broader outreach; (4) engaging in public talks and community forums; and (5) developing buy-in from the audiences needed for organizational success. Implementing these strategies not only reinforces the societal relevance of social behavioral science, but also fosters a more informed and engaged public, bridging the gap between academia and the broader community.
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 | 0.078 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.019 | 0.039 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 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".