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Record W3046525100 · doi:10.5539/ass.v16n8p117

Youth Volunteerism, as Non-formal Education, for Professional and Social Integration of Young Labor Force in Pakistan

2020· article· en· W3046525100 on OpenAlexvenueno aff
Sadaf Taimur, Huma Mursaleen

Bibliographic record

VenueAsian Social Science · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsFormal educationNonprobability samplingAsset (computer security)Public relationsEconomic growthSociologyPolitical sciencePsychologyPedagogy

Abstract

fetched live from OpenAlex

Young people are an asset to Pakistan, and they can play a vital role in the country's social and economic development. Non-formal education to promote volunteering activity in Pakistan can endorse the professional and social integration of the young labor force by preparing them with the labor's contemporary skills. The current study is an attempt to explore the situation in Pakistan and identify: (a) if non-formal education plays a role in youth's engagement in volunteering activity; (b) why young people volunteer; (c) what they can learn through volunteering; (d) the significant barriers which can prevent them from volunteering even after getting the training and opportunities to volunteer. Data was collected using purposive sampling from 4 different nonprofit organizations (NPOs), with city offices in three major cities of Pakistan. NPOs, on their end, collected the data form the youth volunteers working with them randomly using an online survey. The research findings revealed that non-formal education plays a significant role in ensuring young people's engagement in volunteering activity. The findings and recommendations from the study can guide youth initiatives and policies in Pakistan to include systematic and consistent non-formal education programs to promote youth volunteerism in Pakistan.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.015
GPT teacher head0.333
Teacher spread0.317 · 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 designQualitative
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

Citations6
Published2020
Admission routes1
Has abstractyes

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