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Record W7115572525 · doi:10.82396/cjcd.v9i2.3037

Charting Workplace Transitioning Pathways of Generation-Y Human Resources Practitioners

2021· article· en· W7115572525 on OpenAlexaff

Bibliographic record

VenueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland) · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHuman Resource and Talent Management
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsWorkforceHuman resourcesAppealWork (physics)Human resource managementCareer PathwaysWorkforce planningFocus group

Abstract

fetched live from OpenAlex

The purpose of this paper is to present results from a study exploring experiences of Generation-Y Human Resources practitioners as they transition from academia to the workplace. Research findings are from on-line surveys, and individual and focus group interviews with 221 college graduates, 170 supervisors of these workforce entrants, and 42 educators. Emergent from data analysis is a prevailing disconnect between new recruit expectations and organizational realities. Revealed is a need for a more streamlined transition to move new recruits into the workforce in the following areas: assigned workload, strategic accountabilities, establishing internal networks, office politics, mentoring, and conflict management. Proposed is a template for fostering academic-business partnerships that capitalize on learning for and from the workplace to ensure premier experiences are delivered to steer new recruits into the HR profession. This enables new recruits to excel in their career aspirations; and gives business leaders the edge in creating work environments that appeal to the new wave of HR practitioners, hence improving ability to recruit and retain them in a shrinking labour market. With a high premium placed on transition management new recruits enter the workforce with a full complement of competencies to advance and perpetuate organizational prosperity.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.358
Threshold uncertainty score0.669

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.210
Teacher spread0.192 · 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 designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Macrodynamic Analysis (Memorial University of Newfoundland)Same topicHuman Resource and Talent ManagementFrench-language works237,207