Enhancing a process-oriented approach to literacy and language learning: The role of corpus consultation literacy
Bibliographic record
Abstract
Abstract Corpora and concordancing have become much more widely available as researchers recognise that they can significantly enrich the language learning environment. There is still, however, a strong resistance towards corpus use by teachers and learners (Römer, 2006:122). An understanding of the implications and relevance of corpus use for pedagogy may help teachers and learners overcome this resistance, and hence accelerate the process of “percolation” (McEnery & Wilson, 1997:5) or the “trickle down” (Leech, 1997:2) of corpus research to language teaching and learning. The pedagogical context in which learners' consultation of corpora (corpus consultation literacy) can be developed is fundamental in understanding this new literacy and developing it so that it leads to successful language teaching and learning. This paper seeks to investigate the role which corpus consultation literacy plays in enhancing the language learning process and, consequently, aims to establish whether this new literacy can contribute to a process-oriented approach to language learning. Firstly, a theoretical overview of a process-oriented approach to language learning will be outlined, before investigating if corpus consultation can potentially enhance such an approach. This will be supported by evidence from a number of published empirical studies, covering aspects such as learning within a constructivist framework, and the development of cognitive and metacognitive skills through the use of cognitive and developmental tools. Learners' comments from related studies, namely Chambers and O'Sullivan (2004), O'Sullivan (2006), and O'Sullivan and Chambers (2006), which pertain to the learning process and the influence of corpus consultation literacy on this same process, will also be considered. The hypothesis presented here is that corpus consultation literacy can enhance a process-oriented approach to language teaching and learning. It is envisaged that this research will contribute towards the establishment of a sound theoretical and pedagogical foundation for the integration of corpus consultation literacy into language teaching and learning.
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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".