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Record W4405139884 · doi:10.70729/ijser15549

Attrition and Retention

2015· article· en· W4405139884 on OpenAlexaff

Bibliographic record

VenueInternational Journal of Scientific Engineering and Research · 2015
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsAttritionMedicineDentistry

Abstract

fetched live from OpenAlex

One of the biggest assets of any industry or organization is its manpower.In the best of worlds, employees would love their jobs, work hard for their employers, get paid well for their work, have ample chances for advancement and flexible schedules so they could attend to personal or family needs when necessary.However, rising business competition has led to high attrition rates in many sectors and retaining the employees is proving to be a herculean task for most organizations in the modern era of globalization and competitive business.And when these employees leave an organization, the HR manager will have a big challenge to maintain the attrition rate of their organization.HR Manager is not only required to fulfill this responsibility, but also find the right kind of people who can keep pace with the unique work patterns in industry.The toughest concern for an HR Manager is however the Attrition Rate.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.134

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.535
GPT teacher head0.467
Teacher spread0.068 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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