Bibliographic record
Abstract
The late Constructivist Grounded Theorist Kathy Charmaz’s “memo-writing” technique involves writing about how categories are connected to the data. Likewise, artistic memoing is a visual, or multimodal artistic response about how categories are connected to the data. Artistic memoing can capture naturalistic observations about data that are hard to put into words and can provide clarity about how to describe data in a written form by visualizing it first. An artistic memo is not about a researcher—it is about the data that a researcher collects. Given this, there are ethical considerations involved in artistic memoing in studies that involve human participants specifically, so that the artistic memo does not result in an objective representation of a participant. During semi-structured interviews in my doctoral research study, “‘What makes a great story?’: Multidisciplinary and International Perspectives on Digital Stories Created by Youth Formerly in Foster Care in Canada,” I noticed that participants often responded in similar ways to certain digital stories or themes within them. At times, I felt compelled to respond to what they said in an artistic, or visual way, rather than a written way. In this paper, I present a working definition of “artistic memoing,” which I describe in past research. Drawing upon Charmaz’s discussion of “memo-writing,” in this paper, I place artistic memoing as a reflexive technique within Constructivist Grounded Theory 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 | 0.165 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.007 | 0.069 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.014 |
| 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".