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Record W4282983638 · doi:10.25071/1925-5624.40421

Navigating ‘Small Objects of Foreignness’

2022· article· en· W4282983638 on OpenAlexvenueaboutno aff
Geraldine Rossiter

Bibliographic record

VenueTusaaji A Translation Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsCosmopolitanismSociologyHegemonyNarrativeNegotiationUrbanismPoliticsAestheticsSpace (punctuation)Ethnic groupGlobal cityGender studiesMedia studiesAnthropologyArchitecturePolitical scienceSocial scienceVisual artsLawLinguisticsArt

Abstract

fetched live from OpenAlex

Too often, totalizing discourses about the nature of the global metropole attempt to control its social and political story and render it an idealized object rather than a space of discrete subjects and perspectives. Walking the city is an act of social experience that allows the urban wanderer to see what has previously been hidden or inaccessible. Urban rambling reveals the ways in which racial and ethnic minorities have been colonized and marginalized, and how their communities have been rendered invisible as well. This paper examines how the metrolingual and metro-cultural practices of cosmopolitanism combined with the micro-strategies of decolonization serve to provide a counter-place to the dominant space of the Toronto city landscape. I frame this investigation with Dionne Brand’s novel, What We All Long For, as a narrative background, complemented by my own experience of aleatory urbanism through several Toronto neighbourhoods to explore the ways in which individual communities resist and re-negotiate the hegemony of settler-colonial municipal and linguistic practices.

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)0.001
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: none
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.023
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.081
GPT teacher head0.373
Teacher spread0.293 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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Same venueTusaaji A Translation ReviewSame topicLinguistic Variation and MorphologyFrench-language works237,207