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Record W2143890808 · doi:10.5430/bmr.v2n1p18

MTDPNA in Non-Oil International Organisations in Libyan Post Crisis

2013· article· en· W2143890808 on OpenAlexvenueno aff
Ahmed Mustafa Younes, Jim Stewart, Niki Kyriakidou

Bibliographic record

VenueBusiness and Management Research · 2013
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsnot available
Fundersnot available
KeywordsProfitability indexBusinessMarketingWork (physics)Quality (philosophy)Service qualityJoint ventureService (business)Business administrationFinance

Abstract

fetched live from OpenAlex

This paper investigates how non-oil international organisations (NOIO) in Libyan post-crisis assess management training and development programme (MTDP) needs. The current situation of MTDP needs assessment and factors that may influence MTDP are investigated. Questionnaire was distributed to (150) managers from nineteen NOIO. We find that performance and experience were the most common MTDPNA measures. Likewise, poor performance, lack of knowledge, and introduction of new work methods were the most common MTDPNA indicators. Customer dissatisfaction, poor service quality, low profitability, and lack of knowledge were also found that have a positive or negative influence on MTDPNA such as; organisational sectors, size, and ownership. Customer dissatisfaction, poor service quality, low profitability, and lack of knowledge were the most used positive indications for the hotel sector, services, manufacturing, and joint venture organisations. Our findings suggested that MTDPNA should be conducted at different times, and different methods should be used.Also, MTDPNA decisions have to be based in a systematic way rather than targeting single or a group of individuals, and have to be delivered equally.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.063
GPT teacher head0.392
Teacher spread0.329 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2013
Admission routes1
Has abstractyes

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