Digital Object Identifier: Privatising Knowledge Governance through Infrastructuring
Bibliographic record
Abstract
This chapter uses what has become arguably the most ubiquitous piece of thinking infrastructure, the Digital Object Identifier (DOI), as a point of entry to explore the infrastructuring of hegemonic power in knowledge circulation. The chapter opens with a technical explanation of the DOI, followed by a brief history of the formation of the organizations that undergird the DOI. Along with the other metric devices, emerging “norms'' and narratives about the DOI further reinforce its centrality and we spend time debunking these myths. We close by exploring and making visible the relational work that the DOI performs to enable and shape the development of surveillance publishing, a dominant mode of profit and cognitive extraction in the higher education and research market. Bibliographic information: Okune A, Chan L. 2023 Digital object identifier: privatising knowledge circulation through infrastructuring. In Routledge handbook of academic knowledge circulation (eds Keim W et al.), pp. 278-287. London, UK: Routledge.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.026 | 0.024 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.019 |
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; both teacher heads agree on what is shown here.
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".