Using internet data sources to achieve qualitative interviewing purposes: a research note
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.266 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it