Bibliographic record
Abstract
Since the middle of the first decade of this century, several authors have announced the dawn of a new Age, following the Information/ Knowledge Age (1970-2005?). We are certainly living in a Shift Age (Houle, 2007), but no standard designation has been broadly adopted so far, and others, such as Conceptual Age (Pink, 2005) or Social Age (Azua, 2009), are only some of the proposals to name current times. Due to the amount of information available nowadays, meaning making and understanding seem to be common features of this new age of change; change related to (i) how individuals and organizations engage with each other, to (ii) the way we deal with technology, to (iii) how we engage and communicate within communities to create meaning, i.e., also social networking-driven changes. The Web 2.0 and the social networks have strongly altered the way we learn, live, work and, of course, communicate. Within all the possible dimensions we could address this change, we chose to focus on language – a taken-for-granted communication tool, used, translated and recreated in personal and geographical variants, by the many users and authors of the social networks and other online communities and platforms. In this paper, we discuss how the Web 2.0, and specifically social networks, have contributed to changes in the communication process and, in bi- or multilingual environments, to the evolution and freeware use of the so called “international language”: English. Next, we discuss some of the impacts and challenges of this language diversity in international communication in the shift age of understanding and social networking, focusing on specialized networks. Then we point out some skills and strategies to avoid babelization and to build meaningful and effective content in mono or multilingual networks, through the use of common and shared concepts and designations in social network environments. For this purpose, we propose a social and collaborative approach to terminology management, as a shared, strategic and sense making tool for specialized communication in Web 2.0 environments.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.021 | 0.035 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".