Editorial: Socio-technical ecologies: Design for human-machine systems
Bibliographic record
Abstract
consider second-order effects on the host environment ("habitat") and, in consequence, on the embedded 48 systems. This explicitly includes long periods of time where systems of systems form ecologies and 49 co-evolve after deployment. Highly interactive settings, such as an advanced smart city scenario, comprise 50 many heterogeneous systems, e.g., robots and/or artificial agents, crowd-sourced data, social media, 51 location-based applications, swarms of delivery drones, cleaning or gardening robots. The goal is to 52 improve urban life, such as transport systems, healthcare, infrastructure, and services, by collecting and 53 analyzing data from a wide range of sensors and applications. However, many of these systems fail to 54 adapt or serve their intended purpose within the larger socio-technical ecosystem as they are oblivious to 55 the complex dynamics in their immediate context, let alone the effects on cities as larger organisms. It is 56 therefore vital to establish an integral way of designing human-machine systems that form socio-technical 57 ecologies, allowing them to respect and adapt to the ever-evolving context in which they are embedded.We believe this Research Topic constitutes a step towards a more comprehensive, multidisciplinary view 59 of human-machine systems and their design. Our thanks go to all reviewers for their in-depth assessments 60 and to the authors for their contributions.This is a provisional file, not the final typeset article
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.018 | 0.011 |
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".