Re-conceptualising political landscapes after the material turn: a typology of material events
Bibliographic record
Abstract
This paper conceptualises and categorises the various relationships between materiality, discursive construction of landscapes and collective action. Building on both post-structuralist and non-representational geography, and incorporating insights from social systems theory and from evolutionary governance theory, we present a perspective on materiality as shaping landscapes, communities and cultures through different pathways. These pathways might involve the construction of landscape concepts and can potentially affect collective choice in political landscapes of actors and institutions. Five types of material events are distinguished: silent, whispering, vigorous, fading and deadly events. These events constitute the spectrum in which materiality and changes in materiality affect communication and action. Such conceptualisation and categorisations help to avoid setting up a harsh distinction between matter and discourse, or a simple choice for one over the other as ontologically prior.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".