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Record W4286229593 · doi:10.1177/0160323x221115358

Small Town, Short Work Week: Evaluating the Effects of a Compressed Work Week Pilot in Zorra, Ontario, Canada

2022· article· en· W4286229593 on OpenAlexaffabout
Zachary Spicer, Joseph Lyons

Bibliographic record

VenueState and Local Government Review · 2022
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsWestern UniversityYork University
Fundersnot available
KeywordsStaffingWork (physics)Flexibility (engineering)Pilot programOperations managementEngineeringPublic relationsMedical educationManagementNursingMedicinePolitical scienceEconomics

Abstract

fetched live from OpenAlex

On September 1, 2020, the Township of Zorra, Ontario, Canada began a compressed work week pilot project designed to add flexibility for its employees in the wake of the COVID-19 pandemic. Office-based employees who opted into the pilot were given either Monday or Friday off from work and then worked longer shifts for the four remaining days. This field note provides information on the program's design and implementation and reports on the findings of pre- and post-pilot surveys designed to gauge attitudes of workers toward the compressed work week. Results indicate that the pilot was received positively and managed to avoid concerns typically associated with compressed work weeks, namely increased fatigue and staffing challenges. In addition to the evaluation of the pilot, we also provide insight into how organizational scale can aid in the development and design of public sector workplace innovations.

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)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.301

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.052
GPT teacher head0.335
Teacher spread0.282 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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