Adapting vignettes for internet-based research: eliciting realistic responses to the digital milieu
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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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".