Research conceptualization in doctoral and master’s research writing
Bibliographic record
Abstract
Research conceptualization is challenging for doctoral and master’s writers, particularly multilingual students engaging in thesis writing or writing for publication. In doctoral and master’s student writing, research conceptualization appears in three genres: problem statements, research proposals and introduction sections or chapters. Swale’s (1990; Feak and Swales, 2011) CARS model is most often used to analyze conceptualization in these genres. While very useful as an analytical tool, the CARS model does not translate well to pedagogy. I argue that Merriam’s (2009) problem/purpose statement and questions (PPS&Q) format provides a flexible and accessible technique to make the process of research conceptualization visible and to help students focus their research throughout the writing process. Navigating problem formulation and gap spotting requires highly complex literacies and Merriam’s method allows students to begin simply and build complexity. While genre visibility provides a way for doctoral and master’s students to access high-level literacies demands, it can also be formulaic and constraining and needs to be taught with critical awareness.
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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 | 0.138 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".