Learning in Contet: Education and research at the Berger Institute for Work, Family, and Children
Bibliographic record
Abstract
One of contemporary buzzwords in education is learning. Everyone wants learning experiences that are a term that implies that some learning—the kind that is not desirable--is artificial or contrived. Although we are not fans of the distinction between authentic and inauthentic learning, the type of learning that students engage in at the Berger Institute for Work, Family, and Children neatly falls into the more desirable category. When learning is authentic, students engage in real-world problems and issues instead of using manufactured exercises. The goal of authentic learning is to solve a problem. By contrast, learning that fails to clear the hurdle needed to be judged authentic is engaged in for the sake of promoting learning per se, not because there is a need for a solution. At the Berger Institute for Work, Family, and Children, the problems we address are real and for students working at the institute, their learning occurs in pursuit of solutions and answers. The Berger Institute is a research institute at Claremont McKenna College, a four-year liberal arts college that is part of a consortium of higher education that includes four other undergraduate colleges (Harvey Mudd, Scripps, Pitzer, and Pomona) and two graduate universities (Claremont Graduate University and Keck Graduate Institute). Our mission is to conduct research on the many ways to make the demands of work and family more compatible. For example, some of the questions we investigate include: Which work policies are useful to working families? When is on-site child care a good option for employers and employees? What is the effect of maternal employment on young children? How can we convince policy makers to use our data when they craft public policies?
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".