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Hybrid Working in the Canadian Federal Public Service

2025· book-chapter· ng· W7130315595 on OpenAlexaboutno aff
Gülçin Eroksal Ülger

Bibliographic record

Venuenot available
Typebook-chapter
Languageng
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Public serviceState (computer science)Work (physics)Service (business)Public policy

Abstract

fetched live from OpenAlex

With the onset of the Covid-19 pandemic, remote and hybrid working has become a necessity for both the private and public sectors. This study aims to explain general rules of hybrid working system in Canadian Federal public service in historical point of view and discuss its implementation through the example of The Department of National Defense of Canada. The literature review provides an overview of the concepts of teleworking, remote working and hybrid working and how hybrid working evolve in the public sector. In the second section, the study try to explain general characteristics of Canadian public service. In the last section of the study examines the historical background of Hybrid working and its current state in Canada and provides an implementation example from Department of National Defence. This study argues that, hybrid policies accepted by the Canadian Government have general recognition, but they create some disputes between the parties and the demands of parties with influence in the federal public service must be carefully considered.

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)0.002
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 score0.178
Threshold uncertainty score0.954

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0200.010
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.063
GPT teacher head0.277
Teacher spread0.214 · 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
Published2025
Admission routes1
Has abstractyes

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