Data-Based Craft: How Data Scientists Craft Their Data, Models, and Products
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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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".