Community as a Vague Operator: Epistemological Questions for a Critical Heuristics of Community Detection Algorithms
Bibliographic record
Abstract
In this article, we aim to analyse the nature and epistemic consequences of what figures in network science as patterns of nodes and edges called 'communities'. Tracing these patterns as multi-faceted and ambivalent, we propose to describe the concept of community as a 'vague operator', a variant of Susan Leigh Star's notion of the boundary object, and propose that the ability to construct different modes of description that are both vague in some registers and hyper-precise in others, is core both to digital politics and the analysis of 'communities'. Engaging with these formations in terms drawn from mathematics and software studies enables a wider mapping of their formation. Disentangling different lineages in network science then allows us to contextualise the founding account of 'community' popularised by Michelle Girvan and Mark Newman in 2002. After studying one particular community detection algorithm, the widely-used 'Louvain algorithm', we comment on controversies arising with some of their more ambiguous applications. We argue that 'community' can act as a real abstraction with the power to reshape social relations such as producing echo chambers in social networking sites. To rework the epistemological terms of community detection and propose a reconsideration of vague operators, we draw on debates and propositions within the literature of network science to imagine a 'critical heuristics' that embraces partiality, epistemic humbleness, reflexivity and artificiality.
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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 | 0.070 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.009 | 0.087 |
| Scholarly communication | 0.014 | 0.042 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".