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Record W2974587263 · doi:10.5539/res.v11n4p12

Alleviation of ‘Generation Gap’ Through Socio-Economic Issues Involvement

2019· article· en· W2974587263 on OpenAlexvenueno aff
Mohamed Buheji

Bibliographic record

VenueReview of European Studies · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Socioeconomic and Political Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsMindsetClosing (real estate)Generation gapReflection (computer programming)Management scienceSociologyPolitical sciencePublic relationsEconomicsComputer scienceLaw

Abstract

fetched live from OpenAlex

The generation gap has been an issue that is rising in many communities. This paper investigates the challenges of ‘generation gap’ and propose a model for closing it. The synthesis of the literature review defines the types of generation gaps and the factors that increase this gap. The contemporary practices and measures used to close this intergeneration gap are identified. Two main approaches are retrieved as a reflection from the literature reviewed: mindset approach and socio-economic engagement approach. The researcher presents a case study that proposes a theoretical framework about connecting the different generations and engaging them through solving socio-economic issues of common interest. The paper recommends further studies in this line where different generations would be engaged more to share knowledge and values and mitigate risks of further gap widening, while solving their socio-economic problems.

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)0.007
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.004
Scholarly communication0.0050.007
Open science0.0010.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.119
GPT teacher head0.319
Teacher spread0.200 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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