Preparing for a more Inclusive Course : teaching to promote inclusion and celebrate diversity
Bibliographic record
Abstract
"The premise of inclusion should be thoroughly uncontroversial. The job of professors, instructors, and educators of all kinds is to offer each student in their classes the same opportunities to learn and expand their horizons. It is part of the basic definition of what it means to do this job. That educators want all their students to succeed is axiomatic, particularly those who are interested in reading a book of this kind. Nevertheless, the challenges of learning can differ enormously among individuals, and many of those challenges align with their identities, cultural backgrounds, privileges, and capacities. None of these characteristics predicts talent in any discipline. Yet, student success nevertheless correlates with individual characteristics [Caballero et al. 2007, Wei et al. 2018]. In other words, characteristics do not predict talent, but characteristics do relate to success. The inclusion gap is the space between talent and success, and it is created, in part, by obstacles to inclusion that we hope this resource might help reduce. While the idea of inclusion - what we refer to as "inclusion by default" - ought to be obvious, achieving an inclusive learning environment can be challenging. Failures to account for diversity in learning environments can lead to systemic exclusion of students for reasons that are unrelated to their ability, effort, or ambition. This outcome is the antithesis of what educators aim to achieve. As authors of this resource, we recognize that we carry our own biases, learned from lifetimes of living in society. Our shared aspiration to eliminate prejudice cannot heal the lived (and sometimes life-altering) experiences of our students and colleagues in being singled out, called out, or labelled because of their identities. A university course cannot wash away such things either. But it is imperative that university courses should never be places where such exclusion is perpetuated. So, the fundamental goal of this book is to suggest ways to do better using a framework that aligns with fairly common approaches to conceiving, designing, and teaching a university-level course. Perfection, which is subjective in this context in any event, should not be the enemy of progress. As instructors, we are uniquely positioned to make a positive difference in students' lives and careers. It's worth it."-- (Open Library (eCampusOntario))
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".