Bibliographic record
Abstract
Abstract The study of Shakespeare in secondary school literature classes remains a global phenomenon across L2 contexts. Understanding said spaces is important, as the study of Shakespeare is known to expand learner knowledge of normative conventions of academic literacy – this, in service of building the cultural and linguistic capital necessary for learners to succeed on their own terms. However, little is known about how best to research this context so as to assist language learners with their academic literacy needs. To address this gap, this study employs thought modeling – an analytic tool informed by complex dynamic systems theory – to investigate the teaching and learning environment of seven secondary school ESL programs in the Canadian province of Ontario. Mining the educational experiences of 106 participants, this research explores five primary components of the educational landscape: conditions, timescales, interactions, artifacts, and agents. Thematic analyses and descriptive statistical analyses were performed on a dataset comprised of surveys and interviews. This study initiates a framework for continuing research into L2 secondary school Shakespeare studies by identifying and describing substantive avenues of research (i.e., control parameters) informing conditions for best practice and highlights thought modeling as an effective analytic framework for understanding educational dynamics.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".