Bibliographic record
Abstract
Although seminal literature indicates that “chalk and talk” is still the predominant lecture method (Watts and Becker, 2008; Watts and Schaur, 2011; Ongeri, 2017), research more specific to millennials (Carrasco-Gallego, 2017; Leinberger, 2015; Litzenberg, 2010; Morreale and Staley, 2016) indicates multiple challenges for economics instructors who are teaching millennials; it suggests instructors need to adapt their teaching methods. They especially point out that millennials have a different skillset than previous student cohorts. Recently, multiple new teaching methods have been proposed in the economics literature. This paper reviews and discusses the most effective teaching methods specifically targeting millennials. New teaching methods clearly focus on the inclusion of popular culture and media, which are already a salient part of the day-to-day life of students. Improving students’ engagement appears to be a paramount objective in the recent literature. Examples of methods reviewed in this papers are flipped classroom, student-crafted economics experiments, and the use of social media as a medium of instruction.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.183 | 0.093 |
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".