Memory and comprehension of narrative versus expository texts: A meta-analysis
Bibliographic record
Abstract
We acquire a lot of information about the world through texts, which can be categorized at the broadest level into two primary genres: narratives and exposition. Stories and essays differ across a variety of dimensions, including structure and content, with numerous theories hypothesizing that stories are easier to understand and recall than essays. However, empirical work in this area has yielded mixed results. To synthesize research in this area, we conducted a meta-analysis of experiments in which memory and/or comprehension of narrative and expository texts was investigated. Based on over 75 unique samples and data from more than 33,000 participants, we found that stories were more easily understood and better recalled than essays. Moreover, this result was robust, not influenced by the inclusion of a single effect-size or single study, and not moderated by various study characteristics. This finding has implications for any domain in which acquiring and retaining information is important.
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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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".