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Record W4313255758 · doi:10.1016/j.dib.2022.108850

A historical dataset of federal government spending changes for Canada

2022· article· en· W4313255758 on OpenAlexaboutno aff
Syed Hussain, Lin Liu

Bibliographic record

VenueData in Brief · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsInterimGovernment (linguistics)Government spendingFederal budgetNarrativePublic economicsEconomicsBusinessEconomic policyAccountingFinancePolitical scienceFiscal yearWelfare

Abstract

fetched live from OpenAlex

This dataset presents a narrative record of all announced federal government spending changes in Canada between 1949q1 and 2012q1. We use the federal government's budget documents, mostly the budget speech, to document announced spending measures. Other budget documents that we use include the Economic Statements, Financial Statements, Mini Budgets, Interim Budgets, and Economic and Budget Updates. We document the motivation behind each announced measure. Based on these motivations, we classify spending changes as exogenous or endogenous. Exogenous changes are those that are not motivated by contemporaneous economic conditions of the country. Endogenous changes are those that are taken in response to current economic conditions. We also document the size of a change, whether it was intended to be temporary or permanent, the duration of the measure, and the size of the measure that was to be implemented in the same year as it is announced. This is the first dataset for any country that comprehensively presents all spending changes undertaken by a government.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.701
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.148
GPT teacher head0.250
Teacher spread0.103 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2022
Admission routes1
Has abstractyes

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