Distinguishing between Theory, Theoretical Framework, and Conceptual Framework: A Systematic Review of Lessons from the Field
Bibliographic record
Abstract
Across many years of teaching Research Methods and assessing many applications for admission into higher degree studies which require an understanding of theories, principles, strategies and skills needed to complete a higher degree such as a Masters or a PhD, one of the things I have found problematic for many students is the inability to articulate differences between theory, theoretical framework and a conceptual framework for a proposed research project. This paper uses experiential methodology to draw upon my experience in practice, and systematic literature review methodology to draw upon supporting scholarly literature by leaders in the field, to contribute to existing knowledge on the meaning of each of these concepts, and more importantly to distinguish between them in a study of Research Methods, and in particular as they relate to designing a research proposal and a thesis for a higher degree. The primary aim is to help the reader develop a firm grasp of the meaning of these concepts and how they should be used in academic research discourses. The review answers five questions. 1. What does each of these terms mean? 2. When and how should each be used? 3. What purposes does a theoretical framework serve? 4. How do you develop a theoretical framework for your research proposal or thesis? 5. What does a good theoretical framework look like?
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 | 0.068 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".