Bibliographic record
Abstract
Any understanding of ecomedia requires thinking through both the “eco” and the “media,” which means contending with different conceptions of ecology (and environment) and multiple forms of media philosophy. In this chapter, I make a case for building on Félix Guattari’s formulation of “three ecologies” by providing a rigorous ontological theorization of these ecologies rooted in relational materialism. Specifically, I deploy the process-relational philosophy of A. N. Whitehead and the processual semiotics of C. S. Peirce to argue for an ontology of media that is also a form of ontology as media. That is, instead of thinking of media (and ecomedia) as specific kinds of things , the proposed ontology takes mediation to be central to the ongoing constitution of reality, with subjectivation , or the constitution of social and agential relations, and objectivation , or the constitution of material relations, as alternate sides of a dynamic continuum. Conceiving of three ecologies—social, material, and medial-perceptual—provides a holistic, dynamic, and ethically imbued approach to understanding how media are always already ecomedia, and how our work with them is a choice of better or worse forms of ecomediation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".