Toward a Theory of Interdisciplinary Connections: A Classroom Study of Talk and Text.
Bibliographic record
Abstract
In 2001, a survey published by the Association of American Colleges and Universities (AAC & U) revealed that over half of the reported general-education reforms since 1994 included interdisciplinary courses, and over a quarter included paired or linked courses (Ratcliff, Johnson, LaNasa, & Gaff, 2001). The popularity of interdisciplinary is not limited to the undergraduate curriculum; enthusiasm for interdisciplinary classrooms is matched, and at some colleges and universities outstripped, by enthusiasm for interdisciplinary research and interdisciplinary hires. The idea of interdisciplinarity has become so popular that, as one wry observer noted, [any] new dean or college president . . . must include three ideas in all talks: strategic planning, fundraising, and interdisciplinary efforts (Field & Stowe, 2002, p. 261). None of this enthusiasm is entirely new: Columbia University began to offer its influential interdisciplinary Contemporary Civilizations course as early as 1919, and major funding for interdisciplinary research began to pour into universities during the 1960s and 70s (Klein, 1990). The current trend toward interdisciplinarity in higher education began during the late 1980s and has blossomed during the current decade thanks to a number of converging interests, including calls for a more interdisciplinary undergraduate curriculum (Boyer, 1987; Boyer Commis-
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.016 | 0.025 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".