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Record W2297899365

Comparing Public and Private Compensation in Alberta

2013· article· en· W2297899365 on OpenAlexaffabout
Amela Karabegović, Jason Clemens

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsFraser Institute
Fundersnot available
KeywordsGenerosityWagePrivate sectorPublic sectorPensionCompensation (psychology)Labour economicsGovernment (linguistics)Order (exchange)EconomicsEfficiency wageBusinessPolitical scienceFinanceEconomic growthEconomy
DOInot available

Abstract

fetched live from OpenAlex

As Alberta’s provincial government continues to struggle with deficits and as it tries to constrain spending, there is heightened interest in how wages and non-wage benefits (i.e., total compensation) in the public sector compare with those in the private sector. This study replicates a previously used methodology by which to compare wages in the two sectors. It then compares some available non-wage benefits more generally in an attempt to quantify compensation differences between the province’s public and private sectors.This paper is divided into three distinct sections. The first reviews past research comparing the compensation of the public and private sector workers. The second section presents and explains the wage comparisons between the private and public sectors (broadly defined) in Alberta. It also presents a summary of the methodology employed to compare and calculate differences in wages between the two sectors. Finally, the third section compares three available non-wage benefits, namely, pension coverage, the age of retirement, and layoffs, in order to gauge the generosity of non-wage benefits in the private and public sectors.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.365

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0030.001
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.256
Teacher spread0.238 · 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 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

Citations0
Published2013
Admission routes2
Has abstractyes

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