OPTIMIZING OUR NETWORKED LIVES
Bibliographic record
Abstract
We use the term networked optimization to emphasize a sustained but changing set of techniques, technologies, and calculations to decide the best life -- the optimal -- through infrastructures, design, mathematics and engineering. Optimization is a vital concept at a time of critical interest in infrastructural power. Usually defined as doing actions to make the best or most effective use of something, our panel highlights the different uses of the term across the internet. Used as a selling point by many technology companies, optimization means different things to different actors. Perhaps the most important question on this is optimized for who? From Facebook to 5G, our panelists work across technical and theoretical literatures as well as computer science and humanities to identify the social implications of networked optimization. Author #1 examines how Facebook’s personalization ideology is engineered into its infrastructure to influence people’s behaviors to maximize its advertising value. Author #2 looks to the discourses and infrastructure of Google’s cloud computing that promise a form of global social engineering. Author #3 takes these questions out of the cloud and into the next-generation of the Internet, 5G. The promised new wireless infrastructure makes a major shift in the meta-governance of communications and re-consolidates power in network operators. Finally, Author #4 looks for forms of resistance through the development of Protective Optimization Technologies that help people counter efforts to nudge and shape their behaviours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".