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Record W4416596653 · doi:10.1111/1744-7941.70053

Determinants and Effects of Remote Work Arrangements: Evidence From an Employer Survey

2025· article· en· W4416596653 on OpenAlexafffundabout
Tony Fang, Morley Gunderson, John Hartley, Graham King, Hui Ming

Bibliographic record

VenueAsia Pacific Journal of Human Resources · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsUniversity of TorontoMemorial University of Newfoundland
FundersAtlantic Canada Opportunities Agency
KeywordsWork (physics)Flexibility (engineering)Survey data collectionTelecommutingHuman resource management

Abstract

fetched live from OpenAlex

ABSTRACT Remote work arrangements are compelling examples of an organization's ability to utilize digital technology. This study analyzes data from a representative survey of Atlantic Canadian employers to evaluate three phenomena: how remote work evolved during the recent COVID‐19 pandemic; the factors influencing these changes; and the impact of these changes on business outcomes. Our findings suggest that urban firms, technologically advanced companies in certain highly skilled industries, and firms offering greater flexibility for remote work were most likely to enhance remote work practices during the pandemic. For the average firm, an increase in the share of remote work correlated with higher organizational productivity, improved employee performance, and greater new product/service innovation. The primary downside was heightened management complexity. Variations were observed along industry and provincial lines.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.309
Threshold uncertainty score0.614

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.034
GPT teacher head0.340
Teacher spread0.306 · 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
Published2025
Admission routes3
Has abstractyes

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