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Record W2982415487 · doi:10.60770/x88w-kr45

Does a recession affect millennials' career expectations?

2017· dissertation· en· W2982415487 on OpenAlexaffabout
Anja Meier

Bibliographic record

VenueMount Royal University Institutional Repository (Mount Royal University) · 2017
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsMount Royal University
Fundersnot available
KeywordsAffect (linguistics)RecessionPsychologyDemographic economicsSocial psychologyLabour economicsSociologyEconomicsKeynesian economicsCommunication

Abstract

fetched live from OpenAlex

In recent years, scholars have shown an increased interest in understanding how Millennials’ perceptions of entitlement impact both their work and academic lives (e.g., Ng, Schweitzer, & Lyons, 2010). However, there is minimal research on the impact that a recession has on Millennials as they transition from university to the labour market. The purpose of the current project was to gain a better understanding of the impact that the current recession in Alberta has on new graduates’ career expectations. We used a mixed methods design that incorporated both focus group data and questionnaire results from 62 third- and fourth-year business students in Alberta. Interestingly, participants’ awareness of the recession had no impact on career expectations. Results demonstrated that immediate career expectations were driven by perceptions of entitlement, while future career expectations were affected by gender. Specifically, men had significantly higher future career expectations than women, even after controlling for entitlement and recession awareness. These findings can be used to assist universities in helping new graduates set realistic expectations when entering the workforce during a recession. At the same time, businesses can use the current results to tailor their recruiting techniques to target the specific needs and desires of graduating Millennials.

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), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.016
GPT teacher head0.193
Teacher spread0.177 · 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
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
Published2017
Admission routes2
Has abstractyes

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