‘ <b>¡</b> Eso no se dice’!: Exploring the value of communication distortions in participatory planning
Bibliographic record
Abstract
Plans and policies rely on knowledge about communities that is often made by actors outside of the community. Exclusion from the creation of knowledge is a function of exclusion from power. Marxists, feminist, decolonial and postmodernist theorists have documented how the knowledge of some subjects is disqualified based on their gender, race, socio-economic position or a range of other constructed differences. Often, several of these constructions intersect in one person's life, compounding their exclusion in ways that are both relational and structural (Crenshaw, 2017). Participatory planning approaches bring members of the community into contact with planning authorities in an effort to include their voices and interests in official plans. Essential to meaningful engagement in such a process is the participant's ability to turn their ideas into change through the exercise of their agency. When that potential for transformation is missing, participation is tokenistic at best and dangerous at worst (Cooke and Kothari, 2001, Hickey and Mohan, 2004; Forester, 2020). When planners ask people whose agency is restricted by institutional and cultural forms of subjugation to talk about issues that adversely impact them, but over which they have little control, we can create exposures to internal and external risks that we are ill-equipped to mitigate. How can planners work towards social transformation without shifting the burden of speaking truth to power onto community members? One of the ways in which power and knowledge are related is through the complicated process of communication. Reflecting on power and communication in planning practice, this paper contemplates the question: when working with communities that have been historically excluded from the creation of knowledge about themselves, should planners strive for undistorted communication or should the distortion in communication be analysed for what it can tell us about agency and power, and opportunities for resistance and transformation?
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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 | 0.048 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.074 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.009 |
| 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".