Understanding and Applying Research Paradigms in Educational Contexts
Bibliographic record
Abstract
The concept of research paradigm is one that many higher degree research students, and even early career researchers, find elusive to articulate, and challenging to apply in their research proposals. Adopting an ethnographic and hermeneutic methodology, the present paper draws upon our experiences as lecturers in Research Methods over many years, and upon pertinent literature to explain the meaning of research paradigm. The paper elucidates the key aspects of research paradigms that researchers should understand well to be able to address this concept adequately in their research proposals. It offers suggestions on how researchers can locate their research into a paradigm and the justification needed for paradigm choice. With the explicit purpose of helping higher degree research (HDR) students design effective research proposals, the paper also discusses the different research methodologies best suited to conduct research in each of the paradigms discussed.
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 | 0.274 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.014 | 0.116 |
| Scholarly communication | 0.043 | 0.047 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".