The Rhetoric of Big Data: Collecting, Interpreting, and Representing in the Age of Datafication
Bibliographic record
Abstract
Rhetorical studies of science, technology, and medicine (RSTM) have provided critical understanding of how argument and argument norms within a field shape what we mean by “data.” Work has also examined how questions that shape data collection are asked, how data is interpreted, and even how data is shared. Understood as a form of argument, data reveals important insights into rhetorical situations, the motives of rhetorical actors, and the broader appeals that shape everything from the kinds of technologies built, to their inclusion in our daily lives, to the infrastructures of cities, the medical practices and policies concerning public health, etc. Big data merits continued attention from RSTM scholars as our understanding of its pervasive use and its ethos grows, but its arguments remain elusive (Salvo, 2012). To unpack the elusivity of big data, we explore one particularly illustrative case of big data and political, democratic influence: the Cambridge Analytica scandal. To understand the case, we turn to social studies of data to explore the range of ethical issues raised by big data, and to examine the rhetorical strategies that entail big data.
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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 | 0.136 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.024 | 0.160 |
| Scholarly communication | 0.035 | 0.055 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".