How to Analyse the Gap: Lost Knowledge and Migration – An Introduction
Bibliographic record
Abstract
Abstract Knowledge is not only relevant to migrants themselves, who have acquired, moved, translated, or adapted bodies of knowledge throughout history. The spatial dimension of migrating knowledge has two sides, since knowledge always originates from specific local or regional settings, including practical or everyday-life knowledge. In many cases, such bodies of knowledge are transported by migrants with specific agency, depending on the context of migration. However, the history of migrant knowledge is mainly written and understood as a story of negotiation, adaptation or ignorance. Consequently, research on migrant knowledge and its application is usually limited to processes during, and especially after, migration, the places of arrival, the ‘import’ of knowledge through migrants and their adjustment, or the translation of old bodies of knowledge to a new social environment to prevent devaluation or ignorance. The origins of migrating knowledge, however, often remain unexamined. This oversight leaves crucial questions unanswered in understanding the complex processes of knowledge transfer. This special issue is particularly interested in ‘lost knowledge’ as a new field of historical migration studies. This introduction and the contributions ask what happened to the places of origin after the departure of local or regional knowledge agents? How did the outflow of knowledge and ideas affect the development of these places? What coping strategies can be observed to replace or substitute the knowledge lost through outward migration?
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".