AGENCY IN SOCIAL ACTIVITY INTERACTIONS: THE ROLE OF SOCIAL NETWORKS IN TIME AND SPACE
Bibliographic record
Abstract
ABSTRACT This paper explores the relationship between travel behaviour, ICT use and social networks. Specifically, we outline a theory of social action that can inform how ICTs relates to social activity travel and explore the efficacy of this theory in an empirical setting. We begin by outlining two factors that influence the propensity to travel: an individual's will to initiate events with members of one's social network, referred to as agency , and the social accessibility of network members themselves. Social accessibility defines a series of practical constraints for social‐activity travel and agency defines the extent to which an individual will actively work within these constraints to maintain their social network. The theoretical section first unpacks these concepts while embedding them in the research literature, finishing with an operationalisation of agency and social accessibility. Using this theory, the empirical section investigates the relationship between agency, social accessibility, and factors associated with both the respondents and their personal networks. More specifically, we examine how agency levels of interaction are related to differences in demographics, global measures of network structure and composition, and measures of media use, particularly of Internet and telephone. We conclude that individuals who are proximate or more active are more likely to maintain reciprocal relationships, and that more distant or infrequent ties require greater maintenance on the individual's part. We believe that studies of activity‐travel and ICTs will benefit from a theoretical lens that articulates some of the transformative effects of ICTs on travel vis‐à‐vis its effects on social life. Social accessibility and agency can help focus that lens thereby enabling researchers to make potentially more elaborate and realistic models that move beyond the spatial and temporal dimensions into social dimensions.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".