Social media and platform work: Stories, practices, and workers’ organisation
Bibliographic record
Abstract
This article introduces the special issue, ‘Social Media and Platform Work: Stories, Practices, and Workers’ Organisation’. In recent years, platform labour studies have increasingly focused on how the growing platformisation of labour has changed work activities, labour processes, work organising, identities, and collectivities. The literature has highlighted the role of media, communication, and social media in platform labour, but more research is needed on these interrelationships. Precisely, the analysis of platform work is necessary due to its complexity and interest in political, economic, social, cultural, and health terms. Throughout the special issue, different contributions are presented that analyse how the emergence of these new jobs brings a set of inequalities that complexify the notion of ‘work’ and dilute workers’ rights, leading to a precarious situation. The use of social media plays a crucial role in the platformisation of labour as it enables the creation of social relationships between workers but also opens the door to communicating, disseminating their work, and even learning informally and about their work. However, the use of social media can also lead to a precarious combination of platform work and content creation – or cultural production. In this regard, it is worth noting to analyse and approach the relationship between platform work and social networks precisely by addressing both perspectives, considering possible vulnerabilities derived from these situations and situations of precariousness.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".