The Problem of First-Year Seminars: Risking Disengagement Through Marketplace Ideals
Bibliographic record
Abstract
First-year seminars (FYS) have become increasingly prevalent in North American postsecondary institutions. The popularity of such initiatives owes much to the belief that providing unprepared students general life and academic skills can bolster engagement and thereby improve retention. In this paper we argue that, despite their good intentions, many FYS actually perpetuate the kind of disengagement they were designed to alleviate due to their reliance on a narrow, instrumental view of education. To demonstrate, we briefly outline the history and curricula of the FYS movement to draw attention to its dependence on marketplace ideals, rationales, and strategies. We demonstrate some of the ways this vision of education impoverishes the university experience and suggest that, in order to be robust, FYS must focus first and foremost on cultivating rich understandings of the broader purposes of higher education and its relation to the good life, both for and beyond one’s own fulfillment.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".