Connecting the challenges of quality and equality in higher education using the collective intelligence approach: findings from an international expert panel
Bibliographic record
Abstract
This article presents the results of phase one of the B-CAUSE project, an international project designed to connect equity and quality in higher education. Expert stakeholders worked together using collective intelligence methods to develop a shared understanding of (1) key features of equity-focused quality higher education, (2) barriers to equity-focused quality higher education and (3) options for overcoming these barriers. Results highlight the potential transformative dimension to equity-focused, quality higher education, including responsiveness to students, participatory design, pluralism and openness and the educational imperative to promote equity in practice. The barriers and options generated by experts focused on institutional resources and supports, excellence-equity tensions, systemic norms and pressures, reflective complexity, awareness and empathy and student supports. The collective intelligence of experts provides the basis for ongoing research, strategy and pedagogical or curricular innovation as part of the B-CAUSE project and other international efforts to foster equity-focused, quality higher education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".