Diagrammatic Elicitation: Defining the Use of Diagrams in Data Collection
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
The use of graphic representations of experience and the social environment in the data collection process is an emerging approach. The terms diagramming, mapping and drawing are often used interchangeably, with no common interdisciplinary understanding of what they mean. The lack of a unifying terminology has resulted in simultaneous but separate developments undermining a more coherent approach to this emergent method. By defining what a diagram is and examining where diagramming fits amongst other data collection approaches, this paper proposes the term diagrammatic elicitation to refer to the use of diagrams in the data collection process. Two subcategories of this approach include: (a ) participant - led diagrammatic elicitation, where participants create original diagrams and (b ) researcher - led diagrammatic elicitation, where the researcher draws the diagram during the data collection process for discussion or participants edit a researcher - prepared diagram. Establishing these terms will allow researchers to share best practice and developments across disciplines.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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