Science Communication: Synthesis of Research Findings and Practical Advice from Experienced Communicators
Bibliographic record
Abstract
Use of effective public communication strategies is critical for Extension professionals to successfully navigate challenges faced by the agriculture sector and local community, effect policy changes, and ensure public value for the Extension program. Simply addressing the public knowledge deficit is ineffective for gaining public trust in science. Thus, implementation of public engagement and increased dialogue are central to contemporary Extension practice. Such an approach requires balancing factual knowledge with an engaging and open communication style. We draw on both research findings and advice from experienced science communicators to provide a synthesis of practical tips for achieving this balance. Guidance is given regarding framing, word choices, and common pitfalls.
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 | 0.127 | 0.261 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| 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".