Bibliographic record
Abstract
This study adopted a multilevel modeling (MLM) approach to examine the contribution of rater and essay factors to variability in ESL essay holistic scores. Previous research aiming to explain variability in essay holistic scores has focused on either rater or essay factors. The few studies that have examined the contribution of more than one factor to variability in essay scores relied on analytic techniques that do not reflect the nested structure of essay ratings. One goal for this article is to illustrate the use and potential contributions of MLM to research on essay score variability. The study included 31 experienced and 29 novice raters who each rated a set of essays holistically and analytically. Scores were analyzed using MLM to examine the associations between essay features and holistic scores and the impact of rater experience on both essay holistic scores and these associations. The experienced raters assigned lower scores and gave more importance to linguistic accuracy than did the novices. Novices gave more importance to argumentation and their scores exhibited more variability. The article concludes by highlighting the value of MLM in identifying and estimating the contributions of various individual, textual and contextual factors in the rating context to variability in ESL essay scores.
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 | 0.021 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".