Data-Based Craft: How Data Scientists Craft Their Data, Models, and Products
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
In this study, we examine the work of data scientists, members of an emerging technical occupation, through the lens of craft. Drawing on 65 in-depth interviews with data scientists, we show that their work cannot be adequately explained by the human–machine configurations characterized in the existing literature on craft in technical occupations, which primarily focuses on crafting products using ready-to-use tools and ready-to-be-processed materials. Instead, we find that data scientists craft not only their products, but also their tools and materials, often in an iterative and non-linear fashion. This distinct approach entails a unique human–machine-data configuration that we refer to as data-based craft, which stems from the unique nature of digital data and learning algorithms that data scientists simultaneously craft and use. This study advances our understanding of craft in the digital age by highlighting the need to reconceptualize human–machine relationships in data-intensive occupations.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.009 | 0.010 |
| 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