Participation and democratization of knowledge: living theory research for reconciliation
Bibliographic record
Abstract
This presentation is intended to develop ideas from the 8th May 2015 ARNA Town Hall meeting in Toronto to the June 2017 ARNA conference in Cartagena, Colombia. It is focused on emerging understandings of knowledge democracy with convergences among those creating knowledge. We will show how Living Theorists draw on diverse approaches including living-cultures-of-inquiry, participatory frameworks, narrative inquiry, self-study and various forms of action research. Data from epistemologies of the South, East-Asian epistemologies and Western epistemologies, are analysed and used to show the mutual exclusion of different forms of rationality. In contrast to the exclusion expressed as ‘epistemicide’ by de Sousa Santos (2014) the living-logics of Living Theory research are used to show how different knowledges can be reconciled to contribute to the evolution of knowledge for the flourishing of humanity without denying the rationality of a different perspective. Multi-media narratives with digital visual data from a range of professional and community practices are used to clarify and communicate the meanings of embodied expressions of ontological and relational lifeaffirming values. These values are being used as explanatory principles in the explanations of individuals of their educational influences in their own learning, in the learning of others and in the learning of the social formations that influence practice and understandings.
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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 | 0.031 | 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 | 0.010 | 0.087 |
| Scholarly communication | 0.018 | 0.023 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".