The Challenges and Opportunities of Interdisciplinary Research: When LIS Meets Genocide Studies
Bibliographic record
Abstract
As ALISE recognizes in this year’s theme, the positioning of LIS as an increasingly\ninterdisciplinary field represents both a challenge and an opportunity. This is true in my own\nresearch. The questions I ask are only apparent by stepping outside of the confines of LIS’s usual\nconcerns and yet those same questions can only be answered through the insights developed in\nLIS. This is the strength of interdisciplinary research.\nIn my poster, even as I acknowledge this opportunity, I also focus on two challenges I\nface. Sometimes, as with a discipline like genocide studies, perspectives from outside the field\nseem jarring and evoke negative reactions. This is true with my research. The second challenge\nis a chicken-and-egg problem: my work raises questions within genocide studies that few others\nhave addressed. Even as the answers to these questions impact my study, they are outside the\nscope of my research.\nTo explore these opportunities and challenges as I have experienced them, I provide\nbackground on the key concepts I bring from each field, how they relate to one another, and the\nquestions to which this convergence of concepts has given rise. I concentrate on the critiques of\nmy research from within LIS, the problem of questions that need to be left unanswered, and how\nI have used each challenge to further my research. Finally, I use this poster to reflect on how\ninterdisciplinarity affects LIS approaches to research and pedagogy.
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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 | 0.089 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.047 | 0.100 |
| Scholarly communication | 0.063 | 0.071 |
| Open science | 0.004 | 0.050 |
| Research integrity | 0.015 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".