Bibliographic record
Abstract
In formulating an approach to multicultural education, I begin with the premise that all humans have contributed to world development and the flow of knowledge and information, and that most human achievements are the result of mutually interactive, international effort. This premise is also the foundation of the broader epistemological framework associated with peace education. Like multicultural education, peace education, to have integrity, advocates for the non-hierarchical approach to reflexive learning that respects and celebrates a variety of cultural perspectives on world phenomena. Although there are critics of multicultural education such as George Dei (1996) who has written extensively on antiracist education and his ideas are widely used in K–12 programs in Canada. Dei suggests that we promote antiracist education and Appiah’s (2006) resolve that cosmopolitanism better fits the ideas of diversity in people and practice, these theorists do agree that conversation or dialogue is key to negotiating and understanding difference. Dialogue is also a core principle of peace education (Freire 1970, Reardon 1988, O’Sullivan 1999). Peace education, unlike multicultural education, is not ethnocentric and instead examines the diversity of our ecosystem in regards to social and environment justice, believing it is impossible to have one without the other (Clover, Follen & Hall, 2000). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.008 | 0.024 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".