Agency and Amplification
Bibliographic record
Abstract
Computational techniques offer a means to overcome the amplified complexity and resource-intensity of qualitative research on online communities. However, we lack an understanding of how these techniques are integrated by researchers in practice, and how to address concerns about researcher agency in the qualitative research process. To explore this gap, we deployed the Computational Thematic Analysis Toolkit to a team of public health researchers, and compared their analysis to a team working with traditional tools and methods. Each team independently conducted a thematic analysis of a corpus of comments from Canadian news sites to understand discourses around vaccine hesitancy. We then compared the analyses to investigate how computational techniques may have influenced their research process and outcomes. We found that the toolkit provided access to advanced computational techniques for researchers without programming expertise, facilitated their interaction and interpretation of the data, but also found that it influenced how they approached their thematic analysis.
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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 | 0.108 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.008 | 0.050 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 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 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".