Data and Organization Studies: Aesthetics, emotions, discourse and our everyday encounters with data
Bibliographic record
Abstract
Despite the growing “data imperative” and “fetishization of data” across organizational contexts, critical scholars have adhered to a set of normative understandings for how people experience and engage with data and datafication in and around organizations: namely, as numbers and statistics that are “captured”, interpreted, and operationalized. In reality, however, data and datafication are experienced within organizational life in a multiplicity of ways that often have very little to do with numbers and statistics. In this essay, we shift our attention to these less overt and less examined ways in which data and datafication shape organizational life—specifically, the aesthetic, emotional, and discursive aspects of our everyday encounters with it. By attending to the multiple, complex, and nuanced entanglements of data and organization, organizational scholars will be better equipped to navigate the increasingly fraught terrain between technocratic data worship and anti-science politics that characterize the current political moment. In doing so, we hope to contribute to a more politicized, historicized, and democratized data studies that can support movements for social, economic, and ecological justice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".