Mathematics is the method: Exploring the macro-organizational structure of research articles in mathematics
Bibliographic record
Abstract
This article reports the macro-organizational structure of research articles (RAs) in mathematics, based on an analysis of 30 published pure and applied mathematics articles. Math RAs eschew the Introduction-Methods-Results-Discussion (IMRD) structure for an Introduction-Results model that enables researchers to present new knowledge as clearly and succinctly as possible. Notable omissions from the mathematics RA structure are Method and Discussion sections, which mathematicians do not need because of the well-established methodology used in the field (based on deduction and induction) and the relative absence of extended discussion required to interpret research findings. We contextualize the macrostructure of RAs in mathematics within the discourse conventions and disciplinary assumptions about knowledge in the field to suggest the value of such a strategy to teachers and students of academic writing.
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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 | 0.039 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.032 | 0.025 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".