Generative Artificial Intelligence, Interdisciplinarity, and the Global English-Medium Knowledge Economy
Bibliographic record
Abstract
This State of the Inquiry (SotI) critically investigates the implications of generative artificial intelligence (GAI) for interdisciplinary research and scholarly communication within the global English-medium knowledge economy (GEMKE). Anchored in three guiding questions, the article interrogates (1) the extent to which GAI facilitates genuine interdisciplinary knowledge production versus reinforcing entrenched disciplinary silos; (2) how GAI’s dependence on established academic infrastructures influences the visibility and legitimacy of particular interdisciplinary fields; and (3) the impact of automated cross-disciplinary synthesis on the epistemic agency and intellectual labor of human scholars. While GAI holds potential to enhance research efficiency and foster new forms of interdisciplinarity, the outcomes of its integration depend largely on how scholars employ these tools; without critical and contextually informed use, it may contribute to epistemic homogenization and the marginalization of nondominant knowledge systems. The SotI advocates for a critically reflexive and contextually informed approach to the integration of GAI in academic practice, while also recognizing the capacity of scholars—particularly those on the (semi)periphery—to actively shape, adapt, and resist these tools in ways that foster inclusive and transformative interdisciplinary scholarship.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".