Writing Accessible Theory in Ecology and Evolution: Insights from Cognitive Load Theory
Bibliographic record
Abstract
Abstract Theories underpin science. In biology, theories are often formalized in the form of mathematical models, which may render them inaccessible to those lacking mathematical training. In the present article, we consider how theories could be presented to better aid understanding. We provide concrete recommendations inspired by cognitive load theory, a branch of psychology that addresses impediments to knowledge acquisition. We classify these recommendations into two classes: those that increase the links between new and existing information and those that reduce unnecessary or irrelevant complexities. For each, we provide concrete examples to illustrate the scenarios in which they apply. By enhancing a reader's familiarity with the material, these recommendations lower the mental capacity required to learn new information. Our hope is that these recommendations can provide a pathway for theoreticians to increase the accessibility of their work and for empiricists to engage with theory, strengthening the feedback between theory and experimentation.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 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".