Metrics for investigations into L2 knowledge of derivational affixes
Bibliographic record
Abstract
Knowledge of derivational affixes makes an important contribution to second language learners' success when reading. Yet while the effects of some learner variables (L2 proficiency, L1 background) have been investigated, there has been little research addressing the effects of varying characteristics of affixes on their acquisition. The goal of this study was to develop a range of metrics concerning the characteristics of derivational affixes with respect to their frequency of occurrence, semantic salience, and orthographic and phonological form. The study presents 19 metrics (58 when including variants) for 38 frequent derivational affixes. Each metric is calculated across progressively larger vocabulary size levels in recognition of the fact that as learners' vocabulary knowledge develops, their exposure to and knowledge of words including derivational affixes grows. Examples of a selection of metrics for one affix are provided (the full data set being available online; https://osf.io/2vcg9/) as well as some global observations on the data set. It is hoped that these metrics will allow future analyses that provide insights into the process of derivational affix acquisition (by exploring which metrics and to what degree the metrics contribute to acquisition) as well as insights into the order in which affixes are learnt and at what stage in development different affixes are acquired.
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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 | 0.008 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".