Bibliographic record
Abstract
The critical role of the learner in the language learning process has been stressed within recent approaches in the humanities and language studies. For this reason the term learner autonomy is now a very fashionable word in the fields of language learning and teaching. On a general note, there are two dominant approaches to knowledge and learning, each of which adopts a different stance on learner autonomy. These two opposing camps are usually referred to as positivism and constructivism. Although learner autonomy is welcomed by many educators, there is not a broad consensus of opinion on its definition. In this article attempts have been made to show that learner autonomy is a dual conceptualization which incorporates the notions of dependence and independence. The ideology driving the view of learner autonomy presented here has been that learner autonomy should be achieved through the tenets of the scaffolding theory proposed by Bruner (1988). Additionally, the authors will highlight the factors involved in building up autonomy in students. It is hoped that the way we go about dealing with this concept may shed some light on the labyrinth that we are all in, namely the field of ELT.
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 | 0.040 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.015 | 0.036 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.014 | 0.034 |
| Insufficient payload (model declined to judge) | 0.005 | 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".