Adapting vignettes for internet-based research: eliciting realistic responses to the digital milieu
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
Consisting of brief and evocative scenarios, vignettes effectively elicit stimulus responses, examine individual cognitions, and explore novel or sensitive topics. While information and communication technologies have expanded research methods, methodological and ethical considerations for design and implementation of digital vignettes necessitate further attention. This paper considers adaptation of the vignette method to internet-based data collection – particularly with stigmatized and digitally-engaged populations. Critical to vignettes is reproduction of stimuli to elicit realistic responses to particular conditions. For digital vignettes, this includes successful replication of the digital milieu. This paper presents an illustrative example of a digital vignette scenario simulating social media in a mixed-methods, online survey study with lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other sexual and/or gender minority (LGBTQIA+) youth (age 14–24). While digital vignettes may be an effective and appropriate social science research method, numerous methodological and ethical challenges must be considered prior to implementation.
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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 | 0.043 | 0.194 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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