Un diccionario enciclopédico y ontológico: el campo de la migración transnacional
Bibliographic record
Abstract
Abstract: This contribution aims to describe a method of production, organization and exploitation of knowledge allowing the combination in a multilingual terminological work of an encyclopedia and ontology. We chose the domain of TRANSNATIONAL MIGRATION to illustrate the methodology. In the era of globalization, the phenomenon of transnational migration has reached huge proportions: out of the 240 million persons living outside their origin country, close to 120 million would be migrant workers. Such displacements of workers, most of them from countries with labour surplus to countries with labour shortages, have led to new forms of exploitation, causing economic, social, legal and human problems. Labour and social regulations dramatically show inadequacies in solving such problems. The second purpose of this contribution is to present the results of our research in this field. The article MIGRANT WORKER is here to illustrate how information provided by founding documents is extracted and managed in a relational database where each entity refers to another according to the logic of ontology by identified semantic relations. A terminological knowledge base is the end product of this research
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".