A data-driven learning experiment in the legal English classroom using the FLAX platform
Bibliographic record
Abstract
This research presents a data-driven experiment in the legal English field where the FLAX, an open-source self-learning online platform, is assessed as regards its efficacy in aiding a group of legal English non-native undergraduates (divided into an experimental and a control group) to use legal terminology more consistently, amongst other language items. The experimental group were instructed to only resort to the FLAX and to exploit all the functionalities offered by it. Conversely, the control group could access any information source at hand except for the learning platform for the completion of the same task. Two learner corpora were gathered and analysed on a lexical and pragmatic level for the evaluation of term usage and distribution, lexical diversity, lexical fundamentality and the use of discourse markers. The results display a tendency on the part of the experimental group towards a more consistent usage of legal terminology, which also appears to be better distributed than the terms in the non-FLAX corpus. In contrast and on average, the lexicon in the FLAX-based corpus tends to be slightly more basic. Concerning the use of MD markers, the experimental group appears to use, though marginally, a greater number of evidentials, endophoric and interactional markers.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".