Editorial: Imagining and Building a Culturally Inclusive Learning Community
Bibliographic record
Abstract
With the rise of globalization, internationalization, and the interaction and exchange of individuals and organizations across their nations’ borders, there has been an increasing interest in issues related to equity, inclusion, and social justice. Yet strategies for achieving equitable environments that embrace ideals such as safety, diversity, inclusion, and social justice for everyone regardless of their diverse backgrounds (e.g., sexual orientation, ancestry, ability, income, race, and religion), remain elusive in the face of the diverse educational goals. What does this mean for educators? We devote this second volume of the Teaching and Learning Special Issue to this question, while recognizing that creating safe and equitable educational environments is a complex and challenging task, even for those educators who are committed to social justice work (Dei, 2003; McMahon & Armstrong, 2011; Ryan, 2012; Shields, 2004; Solomon, 2002; Theoharis, 2010). In order to create inclusive learning environments, educators need to develop a wide variety of skills, which includes acquiring and mobilizing knowledge, honouring students’ and parents’ voices, bridging cultural boundaries, developing networks and alliances, and accessing needed resources (Armstrong, Tuters, & Carrier, 2012; Ryan, 2012). To facilitate this process, Brock University, Western University, OISE/University of Toronto, and their local school boards in the Niagara, Peel, and Thames Valley districts, developed, organized, and facilitated a series of workshops and conferences in 2012. These events were supported by the Knowledge Network for Applied Education Research (KNAER), and the three participating universities, as well as community and school district partners. What follows in the next section is a description of the conference at Brock University, which first appeared in Volume 7, Issue 2 of this special edition.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".