Getting to Know Ourselves and Others Through the ABC’s
Bibliographic record
Abstract
This book is a valuable resource for teachers and other professionals who are looking for a proven way to increase cultural appreciation and awareness. New applications of the ABCs model of Cultural Understanding and Communication are presented and discussed in this new volume, based on studies done in the United States, and Canada and Europe. In this ground-breaking project, the authors describe how the ABCs model complicated and challenged and changed the cultural perceptions of those who participated in it, even those who were initially highly resistant to such possibilities. At the heart of the project is the exchange of narratives – life stories that give insight into the cultural worlds of selves and others. In addition to the narratives, other instruments including the Transcultural Competence Scale (TCC), provide further evidence of the positive impact of the ABCs on participants’ receptivity toward cultural differences.In the TRANSABCs project, researchers from both sides of the Atlantic invited teacher candidates, students who will become workplace and other professionals to write an autobiography (A) of themselves from various cultural perspectives, a biography (B) of an individual who is culturally different from themselves along particular dimensions, and to use these documents to conduct cross-cultural comparisons (C) between themselves and the person they interviewed. Furthermore, candidates developed culturally responsive ideas for the school or the workplace (C). These exchanges and analyses produced epiphanies and insights that translated into specific actions to improve cultural understanding and communication in classrooms and workplaces. Educators and professionals can take from these examples to inspire their own personal journey toward greater cultural understanding and sensitivity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 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".