Knowledge translation as an interdisciplinary method for information science
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study aims to outline knowledge translation as a method for practising interdisciplinarity in a domain-analytic context which uses new machine learning technologies to improve interoperability between knowledge organisation systems (KOSs). Design/methodology/approach Through conceptual analysis of topics from translation studies and natural language processing (NLP), a theoretical synthesis is performed which applies functionalist theories of translational action to how word embeddings can be used to increase interoperability. Findings Theories from translation studies and recent work in context can inform how information science approaches word embeddings and large language models (LLMs) as tools for furthering interoperability. Originality/value This method for knowledge integration puts concepts like interoperability in a new context and responds to debates about interdisciplinarity in the field of knowledge organisation by proposing a method using machine learning to explore the contexts of different vector spaces.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it