The Citation Economy as a Site of Extraction for Surveillance Publishing
Bibliographic record
Abstract
This paper links the ideas of surveillance capitalism and what Jeff Pooley has described as surveillance publishing with that of the citation economy. A few companies with dominance over academic publishing have been able to capture and use surplus value created through the publishing lifecycle. This extraction—of academic labour, of data, of information—is reinvested into their proprietary data analytics products. This is both literally, as the data collected by the publishing side can be incorporated into data analytics algorithms, and financially, as the profit margins of these academic publishing arms are astonishingly high. Crucially, these profits have been used to expand these companies’ portfolios of extractive data services across industries as academic publishers transition from information vendors to technology-driven data brokers. By providing their labour directly (as editors, reviewers, etc.) or indirectly (as authors) to these companies, scholars are complicit in data collection and analysis used for everything from advertising to law enforcement. This data is sold back to universities who use it to evaluate and surveil the publishing practices of their employees, using proprietary metrics and methods that do not align with principles of academic freedom. This paper provides an overview of this landscape, concluding with implications and recommendations for the scholars and librarians ensnared in it. It also includes a mini-zine we plan to distribute to help contextualize academics’ roles in the citation economy and the ethical implications for their work.
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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 | 0.040 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.015 | 0.039 |
| Scholarly communication | 0.054 | 0.053 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".