Screenplay, Novel, and Poem: The Value of Borrowing From Three Literary Genres to Frame Our Thinking as We Gather, Analyze, and Elevate Data in Applied Ethnographic Work
Bibliographic record
Abstract
Applied ethnography still struggles with the fundamental challenges of (1) framing research to obtain ‘thick’ data, (2) making sense of data in teams and with clients, and (3) making a convincing case with data in challenging environments. We have observed that borrowing from literary genres can be effective in addressing these challenges. We therefore argue that in an age of data science, it is just as important to draw from the literary arts when gathering, analyzing, and elevating evidence to inspire change in applied ethnographic work. We raise three specific applications of literary genres to distinct project phases, to improve how data is collected and analyzed, and how data travels. In this paper we show: (1) how the screenplay can help solve challenges in research framing, to obtain thicker data; (2) how the novel can help solve challenges in analysis, to turn data into meaningful evidence; (3) how poetry can help solve challenges in the opportunities‐development phase of a project, to turn evidence into action.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.009 | 0.043 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".