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Record W174476352 · doi:10.34917/1647583

An Empirical study of attitudes towards telecommuting among government finance professionals

2020· article· en· W174476352 on OpenAlexaboutno aff
Joseph J. Grippaldi

Bibliographic record

VenueDigital Scholarship - UNLV (University of Nevada Reno) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsTelecommutingGovernment (linguistics)BusinessPublic relationsFinanceWork (physics)Political scienceEngineering

Abstract

fetched live from OpenAlex

This paper presents the results of a preliminary study to evaluate attitudes towards telecommuting among finance employees who are employed by special district governments in the United States. Original data was collected by using a self-administered mail survey sent to 400 special district government finance employees who are members of the Government Finance Officers Association (GFOA) of the United States and Canada. This paper will examine variables including employee support for telecommuting, the likelihood of employees working away from the office, and the number of days employees wish to telecommute. A factor analysis was employed to determine if patterns of correlation within the set of observed attitudinal variables could be explained by underlying factors. The results revealed that four factors exist. These include how telecommuting impacts organizational attitudes, personal attitudes, job satisfaction, and the relationship between job stress and saving money. Two of the four factors are analyzed in this paper. Additionally, the impact of telecommuting on the inclination to leave an organization is examined.

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)0.009
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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.061
GPT teacher head0.326
Teacher spread0.265 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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