Bibliographic record
Abstract
Deep-seated educational discourses have blamed low-income communities for their youth’s lack of high school completion. These deficit discourses reflect top–down knowledge hierarchies and a lack of knowledge democracy in education (de Sousa Santos 2007; Hall & Tandon 2017; Visvanathan 2009), and they are in need of critical and diverse knowledge reckoning by low-income communities themselves. This article relays how a community-university participatory action research (PAR) partnership became a dynamic site of knowledge democracy from which to counter and transform deficit-based knowledge systems imposed on economically disadvantaged communities. Steeped in the generative enactments of PAR, storytelling, ecological metaphor, strength-based approaches and the arts, this article explores a low-income/social housing community’s knowledge practices that are energising and growing its community power to support the success of their youth in school. These seven knowledge practices are narrated through the ecological metaphor of trees, specifically via a co-constructed PAR team narrative called the Tree of Community Knowledge and Engagement. In the telling and retelling of this counternarrative-in-the-making, this article embodies knowledge democracy. Here, community members’ energising knowledge practices are recognised as invaluable forms of everyday educational knowing and leadership for their youth. This article further explores three broad ways of knowing that reside within and across community members’ seven knowledge practices: lived knowing, interconnected knowing and participatory/power-in-relation knowing. The three community ways of knowing illustrate how the community is growing its power to support youth’s success via a transformative educational worldview, from which other schools and universities could learn and, indeed, thrive.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.078 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".