Bibliographic record
Abstract
The 2008 financial crisis has prompted specialists to speak about the end of globalisation and the beginning of globality, very much as WWII is said to have catapulted the world from modernisation into modernity. On the economic front, it is resulting in a relative recalibration and even levelling of forces as no single country can boast to be the dominant power anymore. International trade has reached historic levels. As all countries in the world require that companies exporting goods and services to them do so in their national language(s), trade can be carried out only in the language(s) of the target countries. Hence a sharp rise in translation demand. On the social front, we are also seeing some equalisation between cultures and languages. A good example is the first BRIC Summit (Brazil, Russia, India, China) held in 2009, where discussions and deliberations took place through translation and interpretation. In the globalised world, multiculturalism and multilingualism are ever more prevalent. Here again, translation plays a pivotal role: making communications in this multicultural and multilingual world possible. Society's expectations about translation have never been so high. However, major professional and ethical challenges have arisen, especially in view of innovations in the field of information and communication technologies.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".