Millennials: who are they, how are they different, and why should we care?
Bibliographic record
Abstract
Since the publication of Howe and Strauss’s (2000) Millennials Rising, interest in the millennial generation has become widespread, particularly among marketers and employers (Foot, 2001; Hoover, 2009). Companies are eager to tap into a new market that is composed of younger consumers (Nowak et al., 2006), while employers are keen to attract and retain the next generation of workers as the Baby Boomers exit the workforce in large numbers (Burke and Ng, 2006; Perry and Buckwalter, 2010). In the U.S., there are roughly 74.3 million Millennials, representing 23.6 percent of the population (U.S. Census Bureau, 2013). Likewise in Canada, there are 9.1 million Millennials, making up 27 percent of the Canadian population (Statistics Canada, 2011a). Although researchers have used different birth-year boundaries to define the Millennial generation (e.g., 1980–95 in Foot and Stoffman, 1998; 1982–99 in Howe and Strauss, 2000; after 1982 in Twenge, 2010), in reality the exact boundaries defining a generation are much less important than shared historical events and experiences accompanied by social changes (Lyons and Kuron, 2014; Parry and Urwin, 2011). Given the historical events that characterized their lives (e.g., post-Gen X, internet, turn of the century), authors have labeled them Gen Y, Gen Me, Net Gen, Nexus Generation, and Millennial Generation (Advertising Age, 1993; Barnard et al., 1998; Burke and Ng, 2006; Howe and Strauss, 2000; Twenge, 2006). For the purpose of this chapter, we will use the term “Millennial” to keep consistent with the literature.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".