Bibliographic record
Abstract
Interdisciplinary research is the collaboration of people fusing knowledge, theories and methodologies from two or more disciplines. Interdisciplinary collaboration can advance fundamental understanding to form a more inclusive means of examining complex issues beyond the scope of a single discipline. The increase of public monies being dedicated to interdisciplinary research is one way federal agencies like the National Science Foundation are trying to foster more collaboration among people of different disciplines. Data is collected from published articles in the Canadian Journal of Agricultural Economics from 1996 to 2010. Information on authors of each article— occupations, departmental affiliations, positions held, institutional affiliations, and sources of funding—is collected. Since agricultural economics is strongly tied to policy and the increase of funding for interdisciplinary research, I anticipate there will be a rise in the number of interdisciplinary research articles published in the Canadian Journal of Agricultural Economics. I also anticipate that if there are no barriers to joint collaboration between disciplines there will be an increase in the number of tenure track professors engaged in interdisciplinary research. This is a critical issue for professors who are required to publish research in order to receive tenure. This study also has implications for understanding whether difficulties from engaging in interdisciplinary research as opposed to intradisciplinary research for tenure track professors is still relevant.
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 | 0.227 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.034 | 0.028 |
| Scholarly communication | 0.046 | 0.031 |
| Open science | 0.007 | 0.033 |
| Research integrity | 0.014 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".