Writing Abstracts for Research Articles: Towards a Framework for Move Structure of Abstracts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it