The unanticipated performativity of an observation grid through the evolution of its forms of agency
Bibliographic record
Abstract
Purpose This paper explores the unanticipated performativity of an observation grid during the ideation phase of a large construction project. Performativity is conceptualized as the constitutive capacity (anticipated or not) of theory to bring the practice to life through communicational interactions between various actors. Design/methodology/approach The research used the action design research (ADR) methodology to design a grid to observe the facilitation of cross-disciplinary collaborative design workshops during the ideation phase. Key points in the grid’s design and data collection activities were analyzed in line with a communicative constitution of organization (CCO) conceptual framework and a process perspective. Findings Our findings demonstrate how an observation grid, as an other-than-human actor, gives a voice to other other-than-human actors and contributes to the performativity of two practices (research tool design and facilitation), even if the grid did not perform as originally intended. By guiding human actors to understand and resolve what was wrong, in hindsight, the grid worked as intended, even if perceived otherwise initially. Moreover, by considering the grid as a knowledge object, we show that its performativity changes through the evolution of its forms of agency. Originality/value While qualitative research generally perceives observation grids as data collection tools, not as actors, this study focuses on the grid itself and its performativity in the context of two practices: facilitation and research tool design. In addition, we investigate performativity using a Montreal School’s CCO framework that mobilizes knowledge objects.
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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 | 0.058 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.067 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".