The Kids Are Alright – Or, Are They?: The Millennial Generation’s Technology Use and Intelligence – an Assessment of the Literature
Bibliographic record
Abstract
The Millennials (born between 1979 and 1988) have been described in the library and education literature as a unique generation who are more technologically advanced and of higher intelligence than preceding generations. They have also been described as being extremely adept at using Web 2.0 technologies and avid content creators on the Internet. Commentators in the literature suggest that libraries focus their energies on designing services that appeal to this technically sophisticated user group. The authors examine some of the statements made about this generation by leaders in the field of librarianship and education in light of actual data and studies and conclude that these assertions do not seem to be based on solid research. The authors find that the Millennial generation may not be as unique as described; their technical abilities and use of Web 2.0 tools do not seem to be so very different from those of people in older age groups. Also, research into IQ scores and brain development does not positively confirm that this generation is any more intelligent than people in previous generations. The paper concludes with recommendations for libraries and educational institutions that serve this generation. The authors suggest that librarians take a more critical approach when evaluating research about the Millennial generation. In addition, the authors recommend that libraries investigate the true nature of their users through focus groups, surveys, usability studies or other methods so that they can develop services that meet actual needs and abilities.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".