What Good Is Polarizing Research Into Qualitative and Quantitative?
Bibliographic record
Abstract
In education research, a polar distinction is frequently made to describe and produce different kinds of research: quantitative versus qualitative. In this article, the authors argue against that polarization and the associated polarization of the “subjective” and the “objective,” and they question the attribution of generalizability to only one of the poles. The purpose of the article is twofold: (a) to demonstrate that this polarization is not meaningful or productive for education research, and (b) to propose an integrated approach to education research inquiry. The authors sketch how such integration might occur by adopting a continuum instead of a dichotomy of generalizability. They then consider how that continuum might be related to the types of research questions asked, and they argue that the questions asked should determine the modes of inquiry that are used to answer them.
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 | 0.651 | 0.700 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.020 | 0.163 |
| Scholarly communication | 0.067 | 0.097 |
| Open science | 0.007 | 0.027 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".