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Record W3097017830

Middle Class Alterity: A Critical Discourse Analysis of National Economic Identity

2020· dissertation· en· W3097017830 on OpenAlexaboutno aff
Mark Facca

Bibliographic record

VenueYork University Digital Library (York University) · 2020
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial and Cultural Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAlterityIdentity (music)Class (philosophy)Gender studiesCritical discourse analysisSociologyPolitical scienceArtEpistemologyPhilosophyAestheticsLaw
DOInot available

Abstract

fetched live from OpenAlex

Lacking concreteness and context, the use of the middle class in Canadian political discourse of recent years is simultaneously ambiguous and ubiquitous. What is meant by the middle class? Who is a member of this meta-material social stratification? Why is it so prominent in political rhetoric? And, what are the consequences of using the middle class as the primary identity guiding federal fiscal policy? With these questions in mind, this thesis explores the political discourse surrounding the usages of the middle class in an attempt to improve our understanding of how identity is operationalized discursively. Grounded in the theoretical work of Antonio Gramsci, it will be argued that discursively constructed identities, operationalized hegemonically through othering rhetoric, are used in politics to garner mass consent, frame individual/collective identity, and maintain structures of domination and oppression.

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)0.012
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.527

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.005
Science and technology studies0.0260.052
Scholarly communication0.0180.011
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.252
Teacher spread0.228 · 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 designQualitative
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
Published2020
Admission routes1
Has abstractyes

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