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Record W7065590789

Generational differences in work values: Evidence from Canada

2016· other· en· W7065590789 on OpenAlexaboutno aff

Bibliographic record

VenueResearchOnline at James Cook University (James Cook University) · 2016
Typeother
Languageen
FieldPhysics and Astronomy
TopicLaser-Plasma Interactions and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsBaby boomersGeneration xWork (physics)Value (mathematics)Generation yMean value
DOInot available

Abstract

fetched live from OpenAlex

[Extract] We compare the work values of three generational cohorts -- Baby Boomers (born 1945-1964), Generation Xers (born 1965-1979), and Millennials (born in 1980 or later) -- using two samples of working professionals. The two samples were collected in 2002 (N = 1,139) and 2011 (N = 2,707) respectively, using the Lyons Work Value Survey (LWVS). The patterns of intergenerational differences observed within the two samples were examined to assess whether such differences appear to be stable over time. Six work values demonstrated similar patterns of inter-generational mean differences in 2002 and 2011: benefits, supportive supervision, advancement, prestige, co-workers and fun. Another four work values differed significantly among the generations in both samples, but showed differences in the patterns of mean differences: hours of work, balance, challenge, salary, using one’s abilities, and authority. The mean scores for majority of the 19 work value items were lower for the three generational cohorts in 2011 than in 2002: Baby Boomers had lower mean scores on 12 of the items in 2011 relative to 2002; Generation Xers had lower scores on 18 items and Millennials had lower scores on 11 items. These findings provide evidence of stability in inter-generational differences on some work values, while other differences appear to be changing in nature as the various generations move through their life-cycle stages.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.188
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0580.001

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 head0.022
GPT teacher head0.239
Teacher spread0.217 · 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.

Study designNot applicable
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

Citations3
Published2016
Admission routes1
Has abstractyes

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