Millennials: who are they, how are they different, and why should we care?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it