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Record W1838799743 · doi:10.1177/009102601104000101

Competency-Based Management—An Integrated Approach to Human Resource Management in the Canadian Public Sector

2011· article· en· W1838799743 on OpenAlexaboutno aff
Arieh Bonder, Carl‐Denis Bouchard, Guy Bellemare

Bibliographic record

VenuePublic Personnel Management · 2011
Typearticle
Languageen
FieldPsychology
TopicCompetency Development and Evaluation
Canadian institutionsnot available
FundersAustralian Government
KeywordsHuman resource managementStaffingBusinessAgency (philosophy)Public sectorKnowledge managementService (business)Public serviceHuman resourcesService delivery frameworkWork (physics)Performance managementBridge (graph theory)Process managementPublic relationsManagementMarketingComputer scienceEngineeringPolitical scienceSociologyMedicine

Abstract

fetched live from OpenAlex

With a new appreciation for the value of their employees, many organizations are moving rapidly to embrace Competency-Based Management, a relatively new approach to human resource management. Competency-Based Management involves the management of key HR activities such as staffing, learning and performance management, around a competency profile for the work to be carried out. This article describes how Service Canada, a key service delivery agency within the Canadian Federal Public Service, was able to bridge the two worlds of job analysis and competency modeling in order to successfully implement a working competency framework in a large unionized organization.

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)0.005
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.914

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0080.006
Scholarly communication0.0080.003
Open science0.0020.003
Research integrity0.0010.002
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 head0.125
GPT teacher head0.291
Teacher spread0.166 · 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

Citations50
Published2011
Admission routes1
Has abstractyes

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