Bibliographic record
Abstract
Though massive amounts of digital trace data can be collected about how people and information connect online, the question of why they do so has been given less attention. This chapter addresses the spread of innovations and controversies by asking why some actors choose to connect to new issues while others do not? To answer this question, a new framework combining field theory with social network analysis (SNA) – online field theory – is proposed. Field theory suggests that actors in social spaces are unequal and strive to change this inequality, and SNA provides a framework for testing hypotheses emerging from field theory. The framework also draws some elements from actor-network theory (ANT), such as the incorporation of nonhuman actors, but – unlike ANT – empirically examining actor choices mandates the establishment of distinctions between the agency of different categories of actors. These different types of agency interact in mutually constitutive ways with field boundaries: while field structure is readily apparent in Web 1.0 organizational fields – such as the online environmental movement – the lack of information about actor identities in some Web 2.0 settings complicates field analysis. The chapter also examines how online field theory can provide insight into how Web 2.0 algorithmic governance, customization, and personalization are contributing to the emergence of online echo chambers. We define “filter bubbles” as online fields characterized by low contention and high homophily and outline how research into actor connection to innovation should approach them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 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; both teacher heads agree on what is shown here.
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".