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Record W4415479249 · doi:10.32920/30429166

How Does the Government of Canada Apologize: Analyzing the Halifax Regional Municipality's Apology for the Destruction of Africville

2025· article· W4415479249 on OpenAlexaboutno aff
Denée Kiara Rudder

Bibliographic record

Venuenot available
Typearticle
Language
FieldSocial Sciences
TopicMilitary and Defense Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)NeglectRhetorical questionRacismPerceptionPublic policy

Abstract

fetched live from OpenAlex

<p dir="ltr">This major research paper examines how the Canadian government conceptualizes historical trauma and healing in its official apologies. This study focuses on the 2010 apology by Halifax Regional Municipality (HRM) for the destruction of Africville. Africville was founded in the mid-18th century and was a predominantly Black, self-sufficient community. In 1960, the City of Halifax demolished the community. Many have expressed that this was an act of racism after decades of neglect and the placement of undesirable services there. To explore the HRM apology, this paper uses a critical analysis to analyze the rhetorical strategies used and evaluates any post-apology actions. A comparative analysis examines the public's perception of the apology's media coverage. This research sheds light on underrepresented Black Canadian history, the role of apologies in reconciliation, tangible actions toward healing historical wounds, Canada's relationship with apologies, and the media's influence.</p>

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.271
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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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
Published2025
Admission routes1
Has abstractyes

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