Language Curriculum Planning for the Third Millennium: A Future Perspective
Bibliographic record
Abstract
The evolution of language knowledge continues, as does the inquisitive nature of human beings. But the explosive growth of knowledge in the third millennium seems to herald a new era in language teaching. Deepened insights into philosophy have betrayed the poverty of structuralism to account for language learning. The shift from structuralism to poststructuralism has brought about inevitable, though controversial, trends, e.g., the World Englishes and standards movements. Media proliferation of the “mass-age” (McLuhan & Fiore, 2001) of the globalized era has led to a context where appealing terms such as computer-assisted and Internet-assisted language teaching might get blurred sooner in view of more sophisticated advances. Could the future witness virtual reality or expert systems-based language teaching? Language curriculum development in the third millennium should accommodate a recognition of the interdisciplinary knowledge and dynamicity and multimodality of concepts. Starting with the educational philosophy and moving on to related topics, this paper aims at envisaging the putative future of language curriculum development. Each topic in this article has been investigated followed by its effects on its succeeding topic attempting to provide a coherent framework. Glocalization has been introduced as the lost piece of puzzle linking topics coherently.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".