MétaCan
Menu
Back to cohort

Transformational HR for Generation Z

2025· book-chapter· en· W4410026528 on OpenAlexaff
Shivani Dhand, Rimpa Kar, Avtar Singh, Ujjwal Kumar Pathak, Aayushi Pandey, Simranjit S. Randhawa

Bibliographic record

VenueAdvances in computational intelligence and robotics book series · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGenerational Differences and Trends
Canadian institutionsConestoga College
Fundersnot available
KeywordsTransformational leadershipComputer sciencePsychologySocial psychology

Abstract

fetched live from OpenAlex

The contemporary workforce is being radically transformed by the emergence of Generation Z and the explosive growth of the gig economy. Gen Z differs from other generations in valuing flexibility, autonomy, and purposeful work more than the long-term employment model. Their technological literacy has redefined work standards, with freelancing, temporary contracts, and remote work becoming the new norm. While this trend is beneficial in the sense that it provides work-life balance and diversified sources of income, it has challenges in employment security, mental well-being, and career advancement. HR practitioners need to shift by embracing innovation, technology, and flexibility. The old model of employment does not suit Gen Z's needs anymore, calling for dynamic practices such as on-demand learning, customized benefits, and AI-enabled talent management. Yet, aligning digital evolution with a people-centric strategy continues to be the key.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.076
Threshold uncertainty score0.254

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0760.021

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 head0.047
GPT teacher head0.333
Teacher spread0.287 · 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 designTheoretical or conceptual
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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in computational intelligence and robotics book seriesSame topicGenerational Differences and TrendsFrench-language works237,207