Custodians of Rationality: Data Science Professionals and the Process of Information Production in Organizations.
Bibliographic record
Abstract
Adoption and use of epistemic technologies like big data analytics and artificial intelligence in incumbent organizations is believed to increase the descriptive and predictive power of business decisions. Yet, adoption of such epistemic technologies in organizations often involves availing the expertise of data science professionals who generate valuable insights from data for the consumption in business decision-making. Following the recent calls in the information systems scholarship to examine the use of these epistemic technologies in practice, this study examines process of information production by data science professionals in large incumbent banks. In particular, the study documents how the data science professionals generate insights from data, how the insights get consumed in the business decision making, and how rationality manifests in these processes. By demonstrating the mechanisms through which subjectivity and objectivity are intertwined in the production of information, this study contributes to the information systems and organization theory scholarships.
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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 | 0.033 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.054 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".