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Record W2906055431 · doi:10.5539/mas.v13n1p129

Competitiveness of SMEs from the Insertion of Strategic Planning and Human Resource Management as a Tool for Continuous Improvement

2018· article· en· W2906055431 on OpenAlexvenueno aff
Santander De la Ossa, William A. Niebles, Hugo Hernández, Alvaro E. SANTAMARIA, Leonardo Niebles Núñez

Bibliographic record

VenueModern Applied Science · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Applied Research Studies
Canadian institutionsnot available
FundersUniversidad de Sucre
KeywordsBusinessSustainabilityProcess (computing)Work (physics)Process managementResource (disambiguation)GlobalizationHuman resourcesPoint (geometry)Knowledge managementManagementComputer sciencePolitical scienceEconomicsEngineering

Abstract

fetched live from OpenAlex

The orientations in topics of competitiveness suggest at present, that a company obtains greater income when implanting or developing strategies of businesses that incorporate focuses like competitiveness and sustainability. Thus, organizational efforts are being directed in this line of work. Therefore, it is proposed as an objective for this research to know how SMEs is promoting the issue of competitiveness in their organizations and which aspects are the most outstanding of this process. The methodology used was qualitative and based on the documentary review of specialized scientific publications; at the end reflections are made from the holistic approach to highlight the components that contribute most to the subject of cited. The results point to a scenario of greater dynamics in issues of globalization and competitiveness, as the progress made from the research suggests that growth in these topics will undoubtedly be a motor of great importance to achieve important results for any type of organization.

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)0.004
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0070.002
Open science0.0000.003
Research integrity0.0010.001
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 head0.057
GPT teacher head0.344
Teacher spread0.288 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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