Global fairness in digital interaction: A rhizomatic analysis of social imaginaries
Bibliographic record
Abstract
This study addresses and links three features of the social: social imaginaries, social movements and social media. Its aim is to answer the question of whether and how are the social imaginaries of global fairness present in digital interaction.\nThe term ?social imaginary? was first coined by Greek-French philosopher Cornelius Castoriadis (1987) referring not to something unreal or fictitious existing only in the mind of an individual, but to the shared frameworks within which people organise their collective social world. This notion has been revisited throughout time by different scholars, among whom Canadian philosopher Charles Taylor?s work Modern Social Imaginaries (2004) is renowned.\nIn 2009, political scientist Manfred Steger suggests the rise of the global imaginary and within it, the emergence of alter-globalisation imaginaries led by social movement organisations, countering market-driven globalisation (Steger & Wilson 2012; Steger et al. 2013).\nAt the core of this project is the understanding of communication as a tool for change (Mar? S?ez 2012; Chaparro 2015; Tufte 2015) sustained on digital interaction, which is defined as the multi-way communication process mediated by the internet and the use of information and communication technologies (ICTs). It is considered a hybrid type of communication, both individualised and connected at once (Castells 2013).\nWhile a number of studies using both online and offline research methods (Mosca 2014) have analysed current social movement organisations (SMOs), the research is scarce on SMOenabled digital interaction in the public realm. This project aims to fill this gap following for 18 months the Facebook and Twitter accounts of five European social movement organisations and their local branches for Ecuador.\nThe study claims that the structure of digital interaction is rhizomatic, building on the rhizome metaphor as a structure of thought proposed by French philosophers Gilles Deleuze and Felix Guattari (1987). Digital interaction addresses multiple, diverse and scattered concerns, networks, and events, which are apparently disconnected, yet key concepts find linkages and emerge. Digital interaction also affords a multiplicity of languages to communicate simultaneously and asynchronously, which is otherwise unthinkable in face-to-face communication.\nHowever for the analysis of the contents in digital interaction for advocacy, the study takes an approach of grounded theory in search of shared ideas, desires and notions of participants that could portray social imaginaries.Findings illustrate positive and negative affordances of organisationally enabled social media\nfor advocacy purposes, referred throughout as Advocacy 2.0, in parallel to the stages of development of the internet itself.\nThe process followed by Advocacy 2.0 is suggested to be cyclical and composed of four stages: posting, sharing, cooperating and acting. These stages are increasingly demanding and consequently, decreasing in participants. While the first three stages happen completely in the digital world, the last stage of acting refers to both connective and collective (physical) engagement.\nThe analysis proposes that expressivity plays an important role in digital interaction corresponding to the first two stages of this cycle. Heterogenic discourses are not unified, as some are utopian, others dystopian, and many are neutral, disinterested or dispassionate.\nMoreover, discourses in digital interaction are multiple and apparently disconnected. Marketoriented imaginaries stemming from the neo-liberal economic system are tangled with global fairness imaginaries sustained on economic, gender and social equality, environmental conservation and farming practices, trading and politics.\nConsequently, social imaginaries of global fairness are present in digital interaction and can be viewed from the stage of cooperating, suggesting both reflection and involvement in the discussion, to the stage of acting, in which participants commit to collective action in the physical world. Digital interaction enables the connection of people and issues, regardless of place, time and social and cultural differences.\nAdvocacy 2.0 provides the means for people to share their concerns and interact digitally for realising their hope for global fairness.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".