Distributing Knowledge Creation to Include Underrepresented Populations
Bibliographic record
Abstract
This paper documents biases in the creation of knowledge through underrepresentation of diverse populations and population groups in the way research is conducted and published, and subsequently, in the way educational resources are developed and delivered. Research that incorporates the experience of distributed population groups will have greater local applicability, and knowledge published and disseminated in ways that make it available to distributed populations will increase likelihood that the research findings will be incorporated into policy and action across the population. The incorporation of knowledge gained from distributed population groups into the educational experience will enrich it and, like the knowledge, make it more relevant to the whole population. We explore the potential for distributing knowledge creation to contribute in these ways and what changes are required in the way that higher education is organised to maximise distributed knowledge creation, including collaborative co-creation of knowledge and a collaborative capacity-building programme to ensure its sustainability. We propose that the principles described for a distributed university, where education is disseminated largely online through regional hubs to correct local and global inequalities in access, would be suitable to support the development of structures for distributing knowledge creation. Appropriate governance structures should be developed, of which co-creation of knowledge would be an essential component.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".