MétaCan
Menu
Back to cohort
Record W3127466060 · doi:10.1080/13621025.2021.1876640

Malignant citizenship: race, imperialism, and Puerto Rico-United States entanglements

2021· article· en· W3127466060 on OpenAlexaff
Ileana I. Diaz

Bibliographic record

VenueCitizenship Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLatin American and Latino Studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCitizenshipRacializationColonialismRacismGender studiesSociologyRace (biology)PoliticsLegislationPolitical scienceLaw

Abstract

fetched live from OpenAlex

As inhabitants of a US territory, Puerto Ricans experience their American citizenship under a set of constraints, shaped by processes of colonization, imperialism, and racialization. This paper is concerned with thinking through and developing a theorization of citizenship and life in Puerto Rico, by exploring the history and legislation of US citizenship for inhabitants of the island. It posits that the citizenship held by Puerto Ricans is a kind of disguised malignancy that cannot be understood solely by charting the legal history and formal status of the residents of the island. Instead, the citizenship of Puerto Ricans must be understood as a deeply racialized product of centuries of colonization and imperialism, the consequences of which are not easily shed and cannot be accounted for through liberal political theory. Rather, citizenship actually works to simultaneously cement and invisibilize the ways in which Puerto Rican lives are continuously rendered less valuable and their deaths less grievable. Regarding the citizenship of Puerto Ricans, I argue that racialization and racism are inherent to current United States-Puerto Rico relations. As such, this paper articulates ‘malignant citizenship’ as a term which accounts for the colonial/racial foundations and current iterations of citizenship for Puerto Ricans.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.009
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.053
GPT teacher head0.344
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueCitizenship StudiesSame topicLatin American and Latino StudiesFrench-language works237,207