Bibliographic record
Abstract
This study explores the perceptions of employees who have millennial managers. An analysis of employees’ perceptions provides further knowledge to understand how to manage any bias or obstacles that millennial managers may be faced with. This study uses implicit personality theory to understand the subconscious thoughts people have immediately upon meeting them. Semi-structured interviews were conducted with 18 employees. The interviews were then transcribed so the analysis could be conducted with ease using the First and Second Cycle coding. This technique led to the creation of 5 themes: important characteristics and work experience of millennial managers, judgements they face, millennials, baby boomers and finally culture. These themes were utilized to form the results of the study. The first main finding is that age discrimination can be seen within these two sectors, even when participants stated otherwise because their age discriminatory comments were being made subconsciously. The results also showed that millennial managers do face challenges in the workforce, such as being doubted, tested and more. This study’s recommendation is millennial managers should encompass some or all of the important characteristics highlighted by participants to aid in preparing them to overcome said challenges. Presented in absentia on April 27, 2020 at Student Research Day at MacEwan University in Edmonton, Alberta. (Conference cancelled) Faculty Mentor: Theresa Chika-James Department: Human Resource Management NOTE: This work is available to MacEwan users only at https://roam.macewan.ca/islandora/object/gm:2111
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".