Creating Interdisciplinarity: Grounded Definitions from the College and University Faculty
Bibliographic record
Abstract
Definitions of interdisciplinarity often focus on the integration of disciplinary concepts or perspectives. Few definitions, however, are grounded in the work of faculty who conduct interdisciplinary scholarship. To better understand the practice of interdisciplinarity in research and teaching, and its implications for academics' professional lives, I interviewed college and university faculty affiliated with a variety of liberal arts and sciences disciplines in four U.S. institutions of varying size and mission. The study explored how interviewees practiced interdisciplinarity; how institutional, departmental, and disciplinary locations affected their scholarly identities, professional associations, and work lives; and the kinds of rewards they reaped from interdisciplinary work. In this article, I analyze a subset of the interviews, examining explicit and implied definitions of interdisciplinarity and their relationship to faculty members' understandings of disciplinarity and scholarly work. The analysis reveals that definitions of interdisciplinarity that emphasize integration exclude some forms of interdisciplinary work. I therefore suggest an alternative, more inclusive conceptualization that strives to encompass a range of interdisciplinary practices. Further study of interdisciplinary research and teaching might confirm that all interdisciplinary scholarship can be categorized according to the typology described here - or it might provide evidence of additional forms of interdisciplinarity.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.014 | 0.053 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".