Training for Settlement Organizations, English Learners, and Graduate Education
Bibliographic record
Abstract
This chapter focuses on training for settlement organizations, English learners, and graduate education. In the 2015–2016 academic year, members of two different institutions – a community center and a university institute both in Toronto (Canada) – created an intercultural twinnings to enrich the learning opportunities at both institutions. They came together to fulfill the mutual goal of improving their respective educational offerings through an exchange between the employers, employees, and clients/students of their organizations. The joint aim was to improve their pedagogies so as to better meet the needs and interests of their students, who ranged from among the least to the most educationally privileged in Canadian society. Lessons learned from this twinnings were both expected and unexpected. For the university, the original objective of providing opportunities for graduate students to acquire hands-on experience with empirical research was achieved. For the community center, the original goal of acquiring a better understanding of soft skills and how they are taught and can be improved was also fulfilled. The complexities of collaborating across institutions with different cultures surprised both collaborators, as did the richness of language learning opportunities not only for the English as an Additional Language (EAL) students but also for the graduate student researchers, some of whom were still in the process of improving certain aspects of their own academic English skills.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".