The Qualitative Descriptive Approach in International Comparative Studies: Using Online Qualitative Surveys
Bibliographic record
Abstract
International comparative studies constitute a highly valuable contribution to public policy research. Analysing different policy designs offers not only a mean of knowing the phenomenon itself but also gives us insightful clues on how to improve existing practices. Although much of the work carried out in this realm relies on quantitative appraisal of the data contained in international databases or collected from institutional websites, countless topics may simply not be studied using this type of methodological design due to, for instance, the lack of reliable databases, sparse or diffuse sources of information, etc. Here then we discuss the use of the qualitative descriptive approach as a methodological tool to obtain data on how policies are structured. We propose the use of online qualitative surveys with key stakeholders from each relevant national context in order to retrieve the fundamental pieces of information on how a certain public policy is addressed there. Starting from Sandelowski's seminal paper on qualitative descriptive studies, we conduct a theoretical reflection on the current methodological proposition. We argue that a researcher engaged in this endeavour acts like a composite-sketch artist collecting pieces of information from witnesses in order to draw a valid depiction of reality. Furthermore, we discuss the most relevant aspects involving sampling, data collection and data analysis in this context. Overall, this methodological design has a great potential for allowing researchers to expand the international analysis of public policies to topics hitherto little appraised from this perspective.
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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 | 0.200 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".