Misleading Opportunities: a case-study on the influences of global competency frameworks within Ontario initial teacher education
Bibliographic record
Abstract
Initial teacher education (ITE) programmes are called upon to prepare teachers to support K-12 students facing the complex challenges of a globally connected world. One approach to global learning has been through the diverse and contested field of global citizenship education (GCE). More recently, policy actors have introduced global competency frameworks intending to influence educational practice to prepare “youth for an inclusive and sustainable world” (OECD, 2018). This study appraises the influences of Ontario-based global competency frameworks on teacher candidates’ (TCs’) understanding and practice of GCE during their ITE programme. Wary of the longstanding critiques of the “theory-practice gap” in teacher education, the study focuses on how teacher candidates interpret and apply global competency frameworks to guide their learning and pedagogical development during their programme. The qualitative case study critically presents global competency frameworks and examines data collected from TCs’ course assignments and interviews. The analysis of this data foregrounds TCs’ perspectives and is informed by qualitative content analysis and core elements in Canadian GCE. The thesis shows that while TCs adopt the Ontario global competency frameworks as reflective tools that open space for GCE, their interests, experiences, and identities significantly inform their pursuit of GCE in schools. As a tool, the frameworks act as ‘soft levers’ – soft in both the degree of their influence and the kind of GCE they promote (Andreotti, 2006) as they are populated with discursive orientations that emphasize skills for the global marketplace and distract attention from global issues and content. The study draws attention to the limits of approaching ITE research from a “theory-practice” binary. It sheds light on the agentic dimensions of teacher candidates during their teacher education programmes.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".