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Record W4410978917 · doi:10.31542/nn6s1583

Analyzing Alberta's Proposed Exit from the Canada Pension Plan: A Bureaucratic Caring Perspective

2025· article· en· W4410978917 on OpenAlexvenueaboutno aff
Stefan Nasedkin, D Hartman

Bibliographic record

VenueMacEwan University Student eJournal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)BureaucracyPlan (archaeology)Pension planPensionPolitical sciencePublic administrationGeographyComputer scienceLawArchaeologyPoliticsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper explores the potential health impacts of Alberta's proposed withdrawal from the Canada Pension Plan (CPP) on Canadian seniors, focusing on the economic, political, and educational domains of Ray's Theory of Bureaucratic Caring (TBC). We analyze data from Statistics Canada and nursing and health science literature to discern the interplay between CPP income and health outcomes. The economic domain discussion highlights the importance of solidarity and caring for others as a basis for reciprocity in creating a more caring and stable economic environment. The political domain explores how political literacy and nursing advocacy can integrate caring principles into policy decisions. In the educational domain, we examine the role of nursing faculties in fostering political literacy to include caring principles in policy discussions. We propose restructuring nursing curricula to bridge the gap between caring principles and political action. We also advocate for Health Impact Assessments to inform policy decisions, aligning them with Ray's TBC. The implications include the need for informed nursing advocacy, political education, and a caring approach to economic decisions. Future work involves assessing the impact of an Alberta Pension Plan on Albertan and Canadian seniors' well-being and promoting a compassionate and equitable society through care-informed policies.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.254
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
Bibliometrics0.0000.001
Science and technology studies0.0020.000
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 head0.011
GPT teacher head0.259
Teacher spread0.248 · 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
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 routes2
Has abstractyes

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