Social Media Affordances for Connective Action: An Examination of Microblogging Use During the Gulf of Mexico Oil Spill1
Bibliographic record
Abstract
This research questions how social media use affords new forms of organizing and collective engagement. The concept of connective action has been introduced to characterize such new forms of collective engagement in which actors coproduce and circulate content based upon an issue of mutual interest. Yet, how the use of social media actually affords connective action still needed to be investigated. Mixed methods analyses of microblogging use during the Gulf of Mexico oil spill bring insights onto this question and reveal in particular how multiple actors enacted emerging and interdependent roles with their distinct patterns of feature use. The findings allow us to elaborate upon the concept of connective affordances as collective level affordances actualized by actors in team interdependent roles. Connective affordances extend research on affordances as a relational concept by considering not only the relationships between technology and users but also the interdependence type among users and the effects of this interdependence onto what users can do with the technology. This study contributes to research on social media use by paying close attention to how distinct patterns of feature use enact emerging roles. Adding to IS scholarship on the collective use of technology, it considers how the patterns of feature use for emerging groups of actors are intricately and mutually related to each other.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".