Style, content, and the success of ideas
Bibliographic record
Abstract
Abstract From marketers and consumers to leaders and health officials, everyone wants to increase their communications' impact. But why are some communications more impactful? While some argue that content drives success, we suggest that style, or the way ideas are presented, plays an important role. To test style's importance, we examine it in a context where content should be paramount: academic research. While scientists often see writing as a disinterested way to communicate unobstructed truth, a multi‐method investigation indicates that writing style shapes impact. To separate content from style, we focus on a unique class of words linked to style (i.e., function words such as “and,” “the,” and “on”) that are devoid of content. Natural language processing of almost 30,000 articles from a range of disciplines finds that function words explain 4–11% of overall variance explained and 11–27% of language content's impact on citations. Additional analyses examine particular style features that may shape success, and why, highlighting the role of writing simplicity, personal voice, and temporal perspective. Experiments further indicate the causal impact of style. The results suggest ways to boost communication's impact and highlight the value of natural language processing for understanding the success of ideas.
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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 | 0.014 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".