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Record W2726975955 · doi:10.20286/jeas.v1i1.5

Competition Forces Attack Management by New Ways of Managing Conflicts

2016· article· en· W2726975955 on OpenAlexvenueno aff
Nasser Fegh-hi Farahmand

Bibliographic record

VenueNova Journal of Engineering and Applied Sciences · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Development and Regional Competitiveness
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessAsset (computer security)Human resource managementCompetition (biology)Public relationsInternationalizationHuman resourcesAffect (linguistics)PejorativeKnowledge managementPolitical scienceManagementSociologyEconomicsComputer scienceLaw

Abstract

fetched live from OpenAlex

The form and structure of an organization's human resources system can affect employee motivation levels in several ways. Organizations can adopt various new ways of managing conflicts human ware empowerment practices to enhance employee satisfaction. This paper considers the new ways of managing conflicts. The strategic importance of workers is discussed and their interaction, as an asset, with other important organization assets. The basic methodologies for workers are then explained and their limitations are considered. The new ways of managing conflicts revolution moves recording and analysis activities that traditionally professional performance lines of activities focused to high operational content. The scientific and new ways of managing conflicts progress, growth and internationalization of markets, processors are processes in which the accounting profession plays a leading role of new ways of managing conflicts. There has been a longstanding bifurcation between the two with emotions labeled in pejorative terms and devalued in matters concerning the workplace. Keywords : technology management, new ways of managing conflicts human ware, technology factor

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 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.223

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.064
GPT teacher head0.220
Teacher spread0.156 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2016
Admission routes1
Has abstractyes

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