Flooding lagoons, melting mountains: Diffractive vignettes as a communicative engagement with extra-linguistic and response-able materializations
Bibliographic record
Abstract
Purpose: This article aims to illustrate diffractive vignettes as a methodology to extend current communicative and performative approaches. We engage with the critical ethical- onto-epistemological underpinnings of diffraction and explore how it can sensitize communicative research (in particular within the Montréal School (TMS) of CCO scholarship) to processes of materialization - what comes to matter - differently in our research in the context of the climate crisis.Design/methodology/approach: Reading our ethnographic fieldwork concerning the climate crisis in the Venetian Lagoon and the Austrian Alps, diffractive vignettes are introduced as a way through which we ‘thickly perform’ the climate crisis and become response-able for our agential cuts. In crafting these diffractive vignettes, we discuss what experiences and affects materialized and how the climate crisis can emerge differently through the performances in our research.Findings: Our empirical contribution is twofold. First, diffraction helps us attend to how performativity works through extra-linguistic sensibilities and how it moves through affective transmission in the constitution of organizational realities. Second, it allows us to account for the ethical consequence of these materializations by developing response-ability for the materialization and the matterings we produce in our research.Originality: Through a diffractive methodology, we extend a fastly developing body of work on materialization within communicative organizational research with what seems still to be missing from it: an attunement to affective more-than-human Earthly relations and developing response-ability for the particular materialization.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.005 |
| 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".