Bakhtinian dialogism as framework for participant architectural research
Bibliographic record
Abstract
This paper examines a framework for participant architectural research inspired by Bakhtinian dialogism. It does so by testing this approach on a current study of the recently completed Barking Town Square in Barking, England, in the context of urban regeneration in a London suburb struggling with issues of identity. A dialogic framework is derived from two principles of dialogism: entities are described by relations of parts rather than homogeneous wholes; and identities constantly change with respect to the uniqueness of a situation. For the architectural project, this means that participants and objects are defined by their relation with others in the project and that their identity changes over time as these relations evolve. It also means understanding architecture as a complex social process rather than a "thing in itself ”. Finally, the approach assumes the presence of an embodied subject and identifies the researcher as a participant in the architectural process. Participant architectural research therefore implies that research consciously engages in the continuing and dialogic process of giving meaning to a place. Empirical data has been collected using socio-anthropological fieldwork methods of interviewing and participant-observation for the last year and a half. This approach, more attuned to the analysis of social and cultural relations and the "Other”, complements the necessary engagement implied by a dialogic framework.
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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 | 0.054 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.059 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".