A Transcultural Global Systems Perspective: In Search of <i>Blue Marble</i> Evaluators
Bibliographic record
Abstract
Abstract: Ten dimensions of a core culture of evaluative inquiry are identified as themes that emerge from and cut across the diverse articles in this volume. Cross-cultural evaluation emerges as involving mixed methods; integrated epistemologies; politically and institutionally supporting indigenous peoples and cultures; framing cross-cultural intersections, interactions, and integration through an understanding and appreciation of complex ecologies; personal, relational, and institutional reflexivity; and transparent praxis at every level and throughout every aspect of evaluation. Enhancing the capacity of evaluators outside the industrialized world has been important, appropriate, and effective despite major challenges and resource limitations. However, evaluation capacity-building has focused at the nation-state level. Such a focus is important and necessary but inadequate to deal with global issues. The major problems the world faces today and into the future are global in nature. Building on the impressive developments in international and cross-cultural evaluation documented in this special issue of CJPE, the next step and the way forward is to treat the global system as the evaluand and to develop evaluators capable of undertaking transcultural global systems change evaluations. The implications of this new focus are discussed.
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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 | 0.080 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.058 |
| Scholarly communication | 0.030 | 0.017 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".