Diagrammatic Elicitation: Defining the Use of Diagrams in Data Collection
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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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 | 0.151 | 0.245 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".