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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.008
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.082
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