Analyzing Data Generated Through Deliberative Dialogue
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
Deliberative dialogue (DD) is a knowledge translation strategy that can serve to generate rich data and bridge health research with action. An intriguing alternative to other modes of generating data, the purposeful and evidence-informed conversations characteristic of DD generate data inclusive of collective interpretations. These data are thus dialogic, presenting complex challenges for qualitative analysis. In this article, we discuss the nature of data generated through DD, orienting ourselves toward a theoretically grounded approach to analysis. We offer an integrated framework for analysis, balancing analytical strategies of categorizing and connecting with the use of empathetic and suspicious interpretive lenses. In this framework, data generation and analysis occur in concert, alongside engaging participants and synthesizing evidence. An example of application is provided, demonstrating nuances of the framework. We conclude with reflections on the strengths and limitations of the framework, suggesting how it may be relevant in other qualitative health approaches.
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 | 0.061 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it