Unboxing the Black Box: Qualitative Tricks of the Trade for Studying Data-Intensive Work
Bibliographic record
Abstract
As digital data has become integral to organizational life, data-intensive work emerges as a hybrid practice, blending visible actions with less apparent, trace-based processes embedded in digital infrastructures. This transformation is reshaping established professions, such as journalism and healthcare, while institutionalizing new roles like prompt engineers, data scientists, and business intelligence analysts across industries. These developments challenge organizational and management scholars to rethink how to unbox the black box and examine the interplay between infrastructures, worker practices, and the broader organizational and societal contexts in which they operate. This symposium seeks to address these challenges by convening a panel of leading experts whose research intersects with data-intensive knowledge work and qualitative methodologies. The discussion will explore how emerging digital practices complicate traditional methods, the skills researchers must develop to capture contemporary work's fluid and interconnected nature, and the ethical considerations raised by black-boxed systems. By drawing on their own experiences and examining diverse empirical contexts, this session aims to illuminate new phenomena in data-intensive labor while advancing methodological innovation. The symposium will provide attendees with practical insights into evolving research strategies and propose methodological vantage points to sustain scholarly inquiry into the future of work in the digital age.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".