Bibliographic record
Abstract
This study argues that the relationship between new information and communication technologies (ICT) and social movements should be done from a socio-technical perspective. In the present study, we broaden this perspective and use Actor-Network Theory (ANT) to better understand the relationship between social media (as a new ICT) and social movements. From the perspective of ANT, one cannot define unidirectional causal relationships between the social and the technical. New technical developments create opportunities to change the social order and in the meantime technologies are transformed and are adapted differently by humans. Preliminary findings examining the use of Facebook among Iranians, applying the aforementioned relational sociology perspective based on ANT, suggest that the role new ICTs play in social movements and social change is not linear and constant through time. The impact of new ICTs might be different considering different stages in a social movement timeline. In fact, there may be a stage where ICTs actually function as a sort of pressurerelease value, allowing individuals to remain content within the status quo rather than choosing to pursue more radical goals. We propose the utilization of the two concepts of “durability” and “mobility”, from ANT literature, to better understand the potential of online social networking technologies for social change. We suggest three different time stages as short (emergence of movements), mid (development or decline of movements), and late stage (the movement’s continuation, survival or disappearance through time) to be considered in the study of relationship between social media and social change.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".