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Record W4245865558 · doi:10.24124/2011/bpgub1502

Building a culture of engagement across generations

2011· dissertation· en· W4245865558 on OpenAlexaffabout
Miguel Ángel Lucena Romero

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicGenerational Differences and Trends
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsCommitBaby boomersProductivityWorkforcePublic relationsEmployee engagementWork (physics)Organizational cultureBusinessOrder (exchange)MarketingPolitical scienceLabour economicsEconomic growthEconomicsEngineering

Abstract

fetched live from OpenAlex

Canadian companies are operating in an increasingly globalized environment and must strive to become efficient and yet retain talented personnel. Furthermore, as technology continues to increase in complexity, and companies fight for scarce resources, organizations are forced to focus on employee engagement. Employee engagement is defined as the extent to which employees commit to something or someone in their organization, how hard they work and how long they stay, as a result of that commitment ...With constant change and tough economic times on a global scale, Baby-Boomers and some Traditionalists can no longer afford retirement. The result has led to four generations working together. These four generations Traditionalists, Baby-Boomers, Generation X and Y all have different values and expectations, which can be a source of conflict at work. These cross-generational and cross-cultural workforce conflicts can arise and can affect productivity and profits. In order to avoid such conflicts, it is important to identify differences between generations and their motivations and what an organization can do to facilitate a higher level of productivity ...Therefore, my MBA project will determine differences and similarities between the four generations, and will determine what activities can be developed by organizations to encourage and enhance employee engagement within organizations. --P. 3-4

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)0.010
metaresearch head score (Gemma)0.009
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: Qualitative
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.093
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.013
Scholarly communication0.0130.008
Open science0.0010.013
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.079
GPT teacher head0.405
Teacher spread0.327 · 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
GenreOther

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
Published2011
Admission routes2
Has abstractyes

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