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Record W3134497567 · doi:10.55016/ojs/sppp.v12i1.69088

Bloated Bureaucrats or Underappreciated Public Servants? How do Public and Private Sector Wages Compare in Alberta?

2019· article· en· W3134497567 on OpenAlexaffabout
Richard Mueller

Bibliographic record

VenueThe School of Public Policy Publications · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsPublic sectorPrivate sectorCivil servantsBusinessLabour economicsDemographic economicsEconomicsPolitical scienceEconomic growthLaw

Abstract

fetched live from OpenAlex

A year before the United Conservation Party was elected in Alberta, the final budget from the NDP government allocated some $21.6 billion to public sector salaries, wages and benefits, or 38.4 percent of the total expenditures of $56.2 billion (government of Alberta 2018). The same budget estimated a deficit of $8.8 billion.[1] With the general distaste in Alberta for budget deficits, coupled with current low energy revenues and the political improbability of introducing a sales tax as a steady and predictable revenue stream, the province is left with few options to reduce the budgetary shortfall and to keep the debt‐GDP ratio from increasing. If revenues cannot be enhanced, then expenditures must be controlled to reduce any budgetary shortfall. The fact that public‐sector earnings comprise such a large expenditure item means that this compensation is an obvious target for any expenditure‐reduction exercise. The fact that so many Albertans have recently lost lucrative employment in the private sector, while the public sector has remained relatively unscathed, has left public sector workers as obvious targets in any cost‐cutting exercise. In short, many Albertans are demanding that they receive value for their tax dollars when it comes to the provision of public services, and with public sector compensation such a large line item in the budget, it is inevitable that this compensation will be scrutinized. [1] The estimated deficit has since decreased to $6.9 billion (Government of Alberta 2019).

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)0.004
metaresearch head score (Gemma)0.009
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 score0.079
Threshold uncertainty score0.576

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.010
Science and technology studies0.0070.007
Scholarly communication0.0090.003
Open science0.0030.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.061
GPT teacher head0.327
Teacher spread0.266 · 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
Published2019
Admission routes2
Has abstractyes

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