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Record W4321848378 · doi:10.12753/2066-026x-14-212

CHALLENGES AND OPPORTUNITIES IN KNOWLEDGE SHARING IN E-LEARNING PROGRAMS FOR PUBLIC ADMINISTRATION

2014· article· en· W4321848378 on OpenAlexaboutno aff
Ramona camelia Silvestru, Lavinia Nemes, Catalin ionut Silvestru

Bibliographic record

VenueeLearning and Software for Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)Economic growthPolitical scienceGovernment (linguistics)IncentiveEuropean unionLifelong learningBusinessEconomic policyEconomicsManagement

Abstract

fetched live from OpenAlex

The G20 Moscow summit from 2013 highlighted the fact that human resource development remained a major priority for developing countries, especially low-income countries, with important impact on the priorities of other low income countries. When discussing about the current global economic development, about increasing economic competitiveness and reducing economic risks of global crises, we take also into consideration the role that governments and their staff can play in ensuring the adequate implementation of the various policy measures. In order for the government staff to perform at high levels of competence both in high and low income countries, especially in G20 members (Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, the Republic of Korea, Mexico, Russia, Saudi Arabia, South Africa, Turkey, the United Kingdom, the United States of America plus the European Union member states), we consider that continuous education / lifelong learning would be crucial in providing an enabling environment, with e-learning holding a key position, as it enables people, civil servants to deal with future challenges raised by knowledge and information society. In the framework of the technological, normative and procedural evolutions that influence how the staff from public administrations works and possible openness towards e-learning programs, while aware of the various pedagogic, administrative and economic factors that provide incentives as well as drawbacks in using e-learning in providing training to civil servants, we are interested in analyzing e-learning programs developed and used for public administration staff from several G20 states. Our analysis will be focused on assessing the dimensions of the e-learning systems, variety of courses via e-learning platforms, methodologies used in e-learning, possible limitations and challenges in providing e-learning programs to civil servants in several G20 states. The analysis will be conducted using public information available from national agencies with responsibilities in providing such trainings in various G20 states. Our recommendations are oriented towards stimulating the development of an enabling environment for improving inter-agencies and ministerial coordination by intervening at the levels of human resources from the government levels. In this respect, we promote a wider usage of electronic means in lifelong learning for the staff from public administrations and the sharing of information by electronic means aimed at ensuring further human resource development from the public administration. Moreover, we strongly consider that continuous human resource development in the public administration apparatus from the G20 states and knowledge sharing would provide adequate framework for ensuring that government priorities and policy coordination in order to achieve global economic stability, sustainable growth could be achieved, while also contributing to the development of knowledge and information society and economy.

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)0.012
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0060.003
Scholarly communication0.0130.011
Open science0.0020.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.105
GPT teacher head0.347
Teacher spread0.242 · 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 designNot applicable
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
Published2014
Admission routes1
Has abstractyes

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