Supporting Prosocial Behaviour in Online Communities through Social Media Affordances
Bibliographic record
Abstract
ABSTRACT Affordances are action possibilities that emerge from the relationship between the properties of an object and an interacting agent's capabilities. This poster examines how one type of affordance—anonymity—is enabled or constrained by six features of social media platforms. Our work is one step in a broader agenda to: (1) identify affordances that influence users' behaviour in online communities; (2) outline the social media features that enable or constrain those affordances; and, (3) experimentally determine whether certain affordances, or combinations of affordances, support prosocial behaviour. Prosocial behaviour in information and communication technologies (ICTs) is broadly viewed here as benefiting other individuals in online communities, which in turn could increase the subjective well‐being (SWB) of the community's members. SWB is characterized by high positive affect, low negative affect, and high life satisfaction.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".