Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".