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Record W3154629244 · doi:10.3390/jrfm14040183

Draft Methodology of the Age Management Implementation in Human Resource Management in a Transport Company

2021· article· en· W3154629244 on OpenAlexvenueno aff
Martina Hlatká, Ondřej Stopka, Ladislav Bartuška, Mária Stopková, Daniela N. Yordanova, Patrik Gross, Petr Sádlo

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldEngineering
TopicTransport and Logistics Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsCornerstoneHuman resource managementHuman resourcesWork (physics)Process managementKnowledge managementResource (disambiguation)Order (exchange)Computer scienceManagement scienceOperations managementBusinessEngineeringManagement

Abstract

fetched live from OpenAlex

At present, companies should definitely be able to adapt to their environment. It entails being able to successfully predict and eliminate flaws and undesirable steps which may result in negative consequences. It can only be executed by careful consideration of three basic enterprise’s components which comprise the following: material resources, financial resources and human resources. An effective corporate coordination and human resource management is a cornerstone of the enterprise’s success while these components are of the same importance to this success. To this end, the aim of this manuscript is to design innovative recruitment procedures when using age management approach for a specific transport company; in particular, its human resource management is taken into consideration. In the initial parts of the manuscript, an analysis of quantitative and qualitative data is performed, wherein introduction into the addressed subject, relevant literature review, as well as description of utilized data and methods within the conducted research are elaborated. Consequently, in a case study section, the Work Ability Index (WAI) method is used to focus on the chosen group of employees in order to profoundly investigate their work abilities. The very examination of employees’ life cycle encompasses multiple age categories and measures a decrease in their work ability level. As for the ensuing (final) parts of the manuscript, a thorough evaluation of results obtained, appropriate discussion and, last but not least, conclusion section are compiled, in which the most imperative findings of the performed investigation are comprehensively summarized. Following the above, the purpose of this study is to compile a novel methodological procedure in terms of using the principles of age management in human resource management; specifically, in an opted transport company, and thus helping towards more effective and sustainable corporate recruitment strategy.

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)0.021
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.021
Threshold uncertainty score0.112

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.003
Scholarly communication0.0050.002
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0210.004

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 head0.027
GPT teacher head0.283
Teacher spread0.256 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations11
Published2021
Admission routes1
Has abstractyes

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