Beyond the attention economy, towards an ecology of attending. A manifesto
Bibliographic record
Abstract
Abstract We endorse policymakers’ efforts to address the negative consequences of the attention economy’s technology but add that these approaches are often limited in their criticism of the systemic context of human attention. Starting from Buddhist philosophy, we advocate a broader approach: an ‘ecology of attending’ that centers on conceptualizing, designing, and using attention (1) in an embedded way and (2) focused on the alleviating of suffering. With ‘embedded’ we mean that attention is not a neutral, isolated mechanism but a meaning-engendering part of an ‘ecology’ of bodily, sociotechnical and moral frameworks. With ‘focused on the alleviation of suffering’ we mean that we explicitly move away from the (often implicit) conception of attention as a tool for gratifying desires. We analyze existing inquiries in these directions and urge them to be intensified and integrated. As to the design and function of our technological environment, we propose three questions for further research: How can technology help to acknowledge us as ‘ecological’ beings, rather than as self-sufficient individuals? How can technology help to raise awareness of our moral framework? And how can technology increase the conditions for ‘attending’ to the alleviation of suffering, by substituting our covert self-driven moral framework with an ecologically attending one? We believe in the urgency of transforming the inhumane attention economy sociotechnical system into a humane ecology of attending, and in our ability to contribute to it.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".