An Integrative Multi-Dimensional Model of Culturally Relevant Academic Evaluation for the 21st Century
Bibliographic record
Abstract
Due to Covid-19, the world has encountered new challenges regarding pedagogy, learning, assessment, and evaluation. In meeting these challenges, there have been rapid changes in learning, and the gap between pedagogy and evaluation has grown. The purpose of this paper is to develop a new evaluative model suitable for the technologically enhanced, multicultural environment of the 21st century. In this article, we develop a unique multidimensional model of Culturally Relevant Academic Evaluation (CRAE) that fills a gap in the scientific literature on evaluation in higher education. The model depicts evaluation as an integrated process of four dimensions: two of them based on the well-established dimensions of learning and curriculum, and two based on the novel dimensions of inclusive multiculturalism and technology. We consider evaluation in its broad context in higher education, and we analyze the interrelations between the four dimensions of the evaluation process, discussing their contribution to the enhancement of evaluation in higher education.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".