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Trends in Australian and Canadian Public Service Perceptions from an Employee Survey Perspective

2012· article· en· W2163184492 on OpenAlexaffabout
Catherine Althaus, Bryan Evans, Emily Rathbone

Bibliographic record

VenueAustralian Journal of Public Administration · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsToronto Metropolitan UniversityUniversity of Victoria
Fundersnot available
KeywordsPerspective (graphical)PerceptionPublic relationsPublic sectorPublic serviceContext (archaeology)Administration (probate law)Variance (accounting)Consistency (knowledge bases)Political sciencePublic administrationSociologyBusinessPsychologyGeographyAccounting

Abstract

fetched live from OpenAlex

A comparative analysis of results from the 2011 Institute of Public Administration Australia and Institute of Public Administration of Canada surveys of public service leaders is mapped against related public sector employee survey tools results. Alignment of past results with current leader perceptions shows remarkable consistency across the jurisdictions over time. This overarching coherence points to two broad hypotheses: either senior public service leaders possess a common set of preoccupations in the modern global context, or a more critical perspective would question the shortcomings of the instruments given that remarkable change has occurred that one would expect should have driven result variance. Regardless of the conclusion brought to this preliminary analysis, ongoing identification and mapping of senior leader perceptions through such tools is celebrated as an important contribution to ongoing public service organizational health.

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)0.010
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 score0.033
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.007
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.205
GPT teacher head0.452
Teacher spread0.246 · 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

Citations1
Published2012
Admission routes2
Has abstractyes

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