A philosophy of the theory of “acts of citizenship” woven into the fabric of a political anthropology of citizenship
Bibliographic record
Abstract
The significance of Engin F. Isin’s theory of ‘acts of citizenship’ lies not only in its popularity amongst social scientists but, principally, in its engagement with a political anthropology of citizenship. In a collaborative spirit and committed to a political anthropology of citizenship, we critically engage with Isin’s theory, demonstrating how it builds on various normative difficulties that should be acknowledged by anyone using it as methodology, especially for those engaged in anthropologizing the field of citizenship studies. Our examination begins by showing how the theory draws from theoretical works belonging to the causalist school of the philosophy of action, which allows us to untangle the tacit didascaly through which Isin values the introduction of the concept of ‘act’ into our language(s) of citizenship. By tackling its underlying normative bias, we make clear the fundamental element of Isin’s argument: the refusal to reduce citizenship to mere unpurposive processes. Yet, it is unclear, we argue, how citizenship can effectively be captured through purposive processes by investigating ‘acts of citizenship’. Finally, we demonstrate how anthropology allows us to critically address the reductionism at play in the normative distinction between ‘active’ and ‘activist’ citizenship, constituting the very core of Isin’s theory.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.073 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| 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".