MétaCan
Menu
Back to cohort
Record W2551466563 · doi:10.51976/ijari.131317

Life Long Learning System Plays an Important Role in Leading Corporate World

2013· article· en· W2551466563 on OpenAlexaboutno aff
Mehta Jaydip Chandrakant

Bibliographic record

VenueInternational Journal of Advance Research and Innovation · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Learning Practices
Canadian institutionsnot available
Fundersnot available
KeywordsLifelong learningOperationalizationFlexibility (engineering)Public relationsSet (abstract data type)Knowledge managementBusinessPolitical sciencePsychologyManagementComputer sciencePedagogyEconomics

Abstract

fetched live from OpenAlex

In the past several decades, we have witnessed unprecedented social and technological change that has had profound implications for the nature of work. Such acceleration of change necessitates flexibility, the ability and ambition to continuously learn, and a willingness to experiment and take risks. In response, many national governments and industry leaders have emphasized the virtues of facilitating lifelong learning at work. Indeed, facilitating lifelong learning has been touted as a solution to remaining competitive. However, lifelong learning is only a concept. For it to be practical, it must be operationalized into steps from which organizations can follow. The extant research literature is scant in telling us how organizations actually implement lifelong learning practices and policies. Hence, the purpose of this paper is to describe how lifelong learning is grounded in practice. We do this by introducing a new conceptual framework that was developed on the basis of interviews with a number of leading edge corporations from Canada, the USA, India and Korea. At the heart of our model, and any effective lifelong learning system, is a performance management system. The performance management system allows for an ongoing interaction between managers and employees whereby challenging performance and learning goals are set, and concrete plans are made to achieve them. Those plans involve three types of learning activities. First, employees may be encouraged to engage in formal learning. This could be provided in-house, or the employee may take a leave of absence and return to school. Second, managers may deploy their subordinates to different departments or teams, so that they can take part in new work-based learning opportunities. Finally, employees may be encouraged to learn on their own time. By this we mean learning after organizational hours through firm-sponsored 5 programs, such as e-learning courses. Fueled by the performance management system, we posit that these three learning outlets lead to effective lifelong learning in organizations. Our model also stipulates that the three avenues of learning are mutually reinforcing. Formal training may enable an employee to participate in a work assignment in a different department. A work assignment may encourage employees to complete e-learning courses to support their work-based learning. Learning on one’s own time may lead to a promotion, and more formal training. In sum, the three ways of engaging in learning are mutually reinforcing. They are directed by the performance management system to ensure that learning is focused on organizational objectives. This paper provides texture to our theoretical model. We demonstrate how leading organizations use performance management systems to encourage lifelong learning. We also provide examples of how formal training is used to meet organizational goals, how work assignments are leveraged so that individuals have the ability to learn, and how organizations are increasingly providing opportunities for individuals to learn on their own time.

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)0.005
metaresearch head score (Gemma)0.004
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 score0.222
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.001
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 head0.106
GPT teacher head0.463
Teacher spread0.357 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Advance Research and InnovationSame topicHigher Education Learning PracticesFrench-language works237,207