A Guide to Build (ING) Generalised Linear Mixed Model Trees in Canadian Maritime English: Part 1, Social Factors
Bibliographic record
Abstract
ABSTRACT This two‐part guide/research report introduces the Generalised Linear Mixed Model (GLMM) tree analysis technique to variationist sociolinguistics using (ING) variation in Canadian Maritime English (CME) as a test case. GLMM tree analysis combines the advantages of tree‐based recursive partitioning with the ability to include random effects in statistical modelling. In this, Part 1, the GLMM tree technique reveals a more nuanced pattern for age, gender, and education effects on variation between [ɪn] and standard [ɪŋ] for (ING) than simple mixed‐effect regression modelling alone. Linguistic constraints on (ING) variation in this data, as well as the GLMM tree analysis's merits for testing (multi‐)collinear predictors are explored in Part 2.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.025 |
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".