Languages, disciplines, and prefixes: implications and scope of multi, inter, and trans thought
Bibliographic record
Abstract
This presentation sought to reflect upon the prefixes historically used to name those entities which cannot be bounded to a single, fixed, discreet category. Contributions from Linguistics were taken as a starting point to then extrapolate its concepts to the analysis of scientific practice in general. Thus, first, different conceptions of linguistic relations were compared and contrasted: from mono to bi, multi, inter, and finally, translingualism, with special attention to power issues, border demarcation, and speaker characterization in each of these paradigms. Second, a correspondence between analogous categories which describe and study contacts among scientific disciplines (multi, inter and transdiscipline) was tried out. Finally, the reach and limitations of applying this linguistic metaphor to the analysis of hybridization and exchanges within the scientific field were assessed, as well as its potential to induce changes in knowledge classification and production practices
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.052 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".