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Record W4308568240 · doi:10.1027/2698-1866/a000025

The German Version of the Hybrid Work Characteristics Scale

2022· article· en· W4308568240 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychological Test Adaptation and Development · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Toronto
FundersUniversität Bielefeld
KeywordsGermanHuman multitaskingScale (ratio)Computer scienceReliability (semiconductor)Work (physics)Task (project management)PsychologyCognitive psychologyEngineeringLinguistics

Abstract

fetched live from OpenAlex

Abstract. Introduction: To account for fast-paced developments at work, hybrid work characteristics (HWCs) were introduced. To measure them, an English instrument was developed by Xie et al. (2019) . HWCs encompass more than one work characteristics domain such as the task, social, or contextual domain and include boundarylessness, multitasking, the demand for constant learning, and non-work-related interruptions and are associated with employee attitudes and well-being. Objectives: We validated a German translation of the HWC scale. Method: Using employee samples from Germany ( N = 391) and the United Kingdom ( N = 400), we assessed the quality of the German translation. Results: The German version was internally consistent, showed an acceptable model fit, and reached a scalar level of measurement invariance. The HWCs are related to employee attitudes and well-being. Conclusion: We recommend the use of the German translation of the HWC scale, as our results support its reliability and validity.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.171
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.251
Teacher spread0.223 · 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