Writing Abstracts for Research Articles: Towards a Framework for Move Structure of Abstracts
Bibliographic record
Abstract
The abstract as a sub-genre of the research article has been explored in many studies, particularly with regards to its rhetorical move structure. However, these studies have mainly focused on the macro-structures of abstracts in terms of the main moves present based on pre-existing models by Swales (1990), Dos Santos (1996), and Hyland (2000). Studies analyzing the micro-structures of abstracts in which the sub-categories under each main category are lacking. This study identifies the main moves of abstracts, the steps and sub-steps within each move to propose a comprehensive framework for abstract structure. Using a move based analysis, 100 research article abstracts in the field of social science and humanities were analyzed at the sentence level, where each sentence was coded and assigned a move. Based on the analysis, five main moves consisting of 12 steps and 25 sub-steps were identified. The frequency of occurrence revealed that Move 2: Introducing Study and Move 4: Presenting Findings were conventional, while Move 1: Situating Research, Move 3: Describing Methodology, and Move 5: Describing Implication and Recommendation were optional. This study has implications for research on the genre analysis of abstracts as well as the teaching of abstract structure in the academic setting.
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 | 0.062 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.029 | 0.017 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".