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Record W3162517777 · doi:10.1177/08404704211012725

The federal spending power: Building forward after the pandemic

2021· article· en· W3162517777 on OpenAlexaffabout
Fiona A. Miller

Bibliographic record

VenueHealthcare Management Forum · 2021
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsInstitute for Work & Health
Fundersnot available
KeywordsAusterityPandemicGovernment (linguistics)Power (physics)Investment (military)Coronavirus disease 2019 (COVID-19)BusinessPublic administrationPolitical scienceLawPoliticsMedicine

Abstract

fetched live from OpenAlex

The phrase, "the federal spending power," identifies the federal government's ability to spend in areas beyond its constitutional authority to legislate-a power that has supported the development of a national system of universal healthcare coverage in Canada. Even before the COVID-19 pandemic, this power was critical to the expansion of Canada's narrow but deep basket of universally covered services. The challenges exposed by the pandemic mean that still more federal investment will be required. Yet for traditionalists, the material basis of this power is now constrained: the federal government may possess the constitutional authority to invest, but it lacks the fiscal capacity; some form of belt tightening-even austerity-will be necessary. As debates over public spending intensify, health leaders will need to address these questions. Depending on how they do so, health leaders will either support or detract from a healthy recovery.

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)0.015
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.069
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0140.013
Scholarly communication0.0150.018
Open science0.0020.008
Research integrity0.0170.019
Insufficient payload (model declined to judge)0.0150.003

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 head0.040
GPT teacher head0.396
Teacher spread0.355 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2021
Admission routes2
Has abstractyes

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