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Record W2766985609

A Critical Discourse Analysis of Canada's Throne Speeches Between 1935 and 2015

2017· dissertation· en· W2766985609 on OpenAlexaboutno aff
Justin Johnstone

Bibliographic record

VenueMacSphere (McMaster University) · 2017
Typedissertation
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsThroneCritical discourse analysisPolitical scienceLawPolitics
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this thesis was to uncover the tools of manipulation used within political discourses by governments in their attempt to maintain power in society. It specifically asked, How do Canadian federal governments manipulate security, risk, and threat discourses alongside their presentation and understanding of Canadian identity in throne speeches to justify the direction they intend to take the country in with their mandate? This thesis used Critical Discourse Analysis methods to analyze fourteen federal majority government speeches from the throne during the rise and fall of social welfare in Canada. Findings highlight that governments have relatively consistently used the combination of security, risk, and threat discourses between 1935 and 2015. Canadian identity has also been shown to be malleable to government priorities, being connected to notions of collectivism during the rise of social welfare and individualization and productivity during the implementation of neoliberal principles. The introduction of the promise of job creation within the speeches was found to correlate with the introduction of neoliberal principles in Canada. These findings highlight the importance of critical understanding of dominant discourses in society in order to overcome the power they can impose over non-dominant groups.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.750
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0860.000

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 head0.021
GPT teacher head0.268
Teacher spread0.247 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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