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Record W1606951780 · doi:10.1111/pops.12029

Expectations Among Aboriginal Peoples in <scp>C</scp>anada Regarding the Potential Impacts of a Government Apology

2013· article· en· W1606951780 on OpenAlexaff
Amy Bombay, Kimberly Matheson, Hymie Anisman

Bibliographic record

VenuePolitical Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsCarleton University
Fundersnot available
KeywordsForgivenessPessimismSocial psychologyGovernment (linguistics)PerceptionPsychologyPopulationPolitical scienceSociologyDemographyTheology

Abstract

fetched live from OpenAlex

After continued pressure, the C anadian government offered an apology to A boriginal peoples for its role in the I ndian R esidential S chool ( IRS ) system, where children were removed from their families in an effort to assimilate the A boriginal population. Although the apology was sought after, it was unclear what A boriginal peoples expected it to accomplish in relation to their treatment and quality of life within C anada. Quantitative and qualitative analyses revealed that, although A boriginal adults ( N = 164) felt the apology could potentially be a first step towards improved relations with the government and non‐ A boriginal C anadians, expectations that such changes would actually come to fruition were generally pessimistic. In exploring predictors of such expectations, path analysis indicated that those who had been intimately impacted by IRSs reported greater perceived discrimination that, in turn, was associated with lowered intergroup trust and forgiveness. Those who perceived high levels of discrimination were less likely to expect changes following the apology, which was mediated by the low levels of intergroup trust and forgiveness towards the government, but not towards non‐ A boriginal C anadians. Essentially, an apology was not enough to elicit hope for improved intergroup relations, especially when perceptions of continued discrimination impeded the restoration of intergroup trust and forgiveness.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.720
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.329
Teacher spread0.318 · 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 designObservational
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

Citations23
Published2013
Admission routes1
Has abstractyes

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