Bibliographic record
Abstract
The two past decades have witnessed enormous changes in many aspects of our lives, often with respect to new uses of technology. While these technological innovations are chiefly beneficial, they may also present challenges to us as language teachers, when we are expected to incorporate them into our professional lives. Other challenges are subtler, having to do with teacher/student roles and expectations: the “average” student and the ways they interact with their teachers is often very different now from 20 or 30 years ago. Twenty first century students expect and are expected to take in greater responsibility for their own learning much of which may take place outside the classroom as new uses of technology obviate the need for everyone to be in the same place at the same time; as autonomous learners, student´s relationships with their teachers are often hard to define. The role of the teacher has evolved over the years to the point where we are not considered so much as “the” expert, but rather as partners in the learning experiences or even as moral agents for change. All of these changes and innovations, whether they be concrete and practical or theoretical and attitudinal, have had an impact on the role(s) of the language teacher. By the same token, English Language Teaching Pedagogy has gone through various transformations. It is up to us as language professionals to decide how we will meet the challenge of change and how what we do in the classroom will evolve as result.
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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.055 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".