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Record W3119560438 · doi:10.5430/bmr.v9n4p35

Human Resource Case Analysis – The Students’ Perspective

2021· article· en· W3119560438 on OpenAlexvenueno aff
Marija Runić Ristić, Menatalla Hosam Korany Ahmed Ali, Shareefa Reda Alkhateeb, Cam Caldwell, Verl Anderson

Bibliographic record

VenueBusiness and Management Research · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Marketing Education
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)Human resource managementKnowledge managementHuman resourcesKey (lock)Work (physics)Resource (disambiguation)PsychologyProcess managementBusinessComputer scienceManagementEngineeringEconomics

Abstract

fetched live from OpenAlex

Case analysis is a powerful teaching tool that requires students to demonstrate the higher-level application, analysis, evaluation, and synthesis skills essential to effective critical thinking. Human Resource Management (HRM) professionals must develop these skills and incorporate them into their day-to-day practices in serving organizations and the employees with whom they work. This paper presents HRM students’ perspectives about ten key factors essential to developing case analysis competencies essential to prepare them for successful careers.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.035

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 studies0.0020.007
Scholarly communication0.0070.006
Open science0.0010.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.057
GPT teacher head0.377
Teacher spread0.320 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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