Shifting data cultures: From industrial homogenization to heterogeneous framings
Bibliographic record
Abstract
This article investigates change in data cultures from an empirical and epistemological perspective. It critically examines the notion of acculturation, which assumes a uniform and dominant data culture is disseminating across various professional sectors to foster digital maturity. I contend that this view is flawed because it presupposes that homogeneous data cultures spread linearly from centers of power (major data industry players such as Microsoft or governmental entities) to peripheral sectors. In opposition to this simplistic view and other reductive theories such as diffusionism, evolutionism, and essentialism, I adopt a pragmatic approach to investigate changes in data cultures by analyzing their diverse framings and inflections. Through this analytical lens, I scrutinize adherence, discrepancies, shifts, and tensions among different interpretations of data culture as evidenced in data practices. Drawing from a case study of the National Library and Archives of Quebec (Canada), I demonstrate the diverse interpretations of organizational data culture within the institution. These interpretations hinge on three main framings of data practices that correspond with the institution’s missions: documentation of artifacts, public accessibility, and organizational management. These framings undergo several shifts associated with major dynamics in digital environments—datafication, discoverability, and platformization—that reshape the meanings of data culture. Beyond the empirical findings, this article contributes to critical data studies by offering an epistemological insight into the shifts and power dynamics within data cultures, conceptualized as complex interplays of meanings, material arrangements, and social practices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.001 | 0.001 |
| 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".