Using internet data sources to achieve qualitative interviewing purposes: a research note
Bibliographic record
Abstract
In this research note, we examine the function, merits, and challenges of using internet data sources, namely, social media discussion analysis and email interviewing, alongside data collected for the same study from traditional face-to-face interviewing. This comparison opportunity arose from recruitment challenges in our study, which investigated kindergarten teachers’ perspectives and experiences with play-based teaching in kindergartens. Although we had planned to use only face-to-face interviewing, recruitment challenges prompted the use of other data to examine the same research objective, allowing us to analyze the data from each method side-by-side. We contend that social media analysis and email interviewing offer complementary benefits to approaches currently available for qualitative researchers, especially when recruitment attempts through traditional methods fail. This article focuses on practical and practice-based aspects, for qualitative researchers who are seeking alternative research methods to collect rich data about participants’ perspectives and experiences.
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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 | 0.332 | 0.299 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".