Big data audiences: Critical approaches to the datafication of audience ontologies in contemporary media industries
Bibliographic record
Abstract
The current transformations in how audiences are datafied, including how that data is then traded and sold, used in various algorithms and AI models, and executed on to shape our media ecosystems, necessitate renewed frameworks for conceptualizing these datafied audience ontologies, which we refer to in this issue as ‘big data audiences’. Big data audiences are the lifeblood of the contemporary digital media ecosystem, with significant epistemic and cultural consequences that social scientists and humanists have not sufficiently grappled with. While political economy remains central to understanding the contemporary contexts of audience datafication, this issue demonstrates how theories and methods from the domains of social and humanistic research are equally essential for conceptualizing big data audiences. The issue brings together a range of methodological approaches and aims to catalyze critical media scholarship on audiences that explores the industrial and cultural aspects of datafied audience ontologies in media industries big and small across the globe.
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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 | 0.048 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.016 | 0.127 |
| Scholarly communication | 0.030 | 0.074 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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".