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Record W4406917809 · doi:10.70599/rvim/2024/346

Assessing the Effectiveness of Training and Development Initiatives in Enhancing Workforce Performance and Operational Efficiency of the Organization in Selected IT Companies in Bangalore

2025· article· en· W4406917809 on OpenAlexaff
D. Hemalatha, Vinith Kumar J

Bibliographic record

VenueRVIM journal of management research. · 2025
Typearticle
Languageen
FieldPsychology
TopicHuman Resource Development and Performance Evaluation
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsWorkforceTraining (meteorology)BusinessWorkforce developmentTraining and developmentEmployee developmentProcess managementOperations managementKnowledge managementEngineering managementEngineeringEconomic growthManagementComputer scienceEconomicsGeography

Abstract

fetched live from OpenAlex

Learning impacts the performance of the employees. The learning of the employees will be influenced by the Training and Development programs of the organizations. Training programs will enhance decision-making skills, interpersonal skills and Operative skills. A survey has been conducted with the help of a sample of 96 employees from 14 IT companies of Bangalore and it is found that most of the employees are feeling positive with the Training programs. Most of the respondents feel that Training content and job performance are relevant. The employees who are more engaged are more confident of delivering their duties in the job as the skillset is updated as per the changing environment. The employees getting more support from the management are more motivated and able to perform well in the 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)0.002
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.082
GPT teacher head0.403
Teacher spread0.320 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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