Democratic Organizations and Their Monstrous Digital Self: The Use of Facebook by a Labour Union
Bibliographic record
Abstract
This paper investigates how existing democratic organizations may be altered by members’ participation on social media. To do so, over a two-year period we studied a labour union that opened a Facebook group to modernize its democratic functioning. Unexpectedly, union leaders came to take seemingly completely undemocratic decisions, such as banning members from the Facebook group. To make sense of this ‘surprising’ decision, we describe how both union leaders and online participants socially construct this online–offline assemblage. We argue that three types of interpretation have played a pivotal role in this process: the perceived harmony between online and offline processes, their perceived controllability, and the legitimacy attributed to the ‘others’. We then theorize the online-offline interaction within the union democratic process as a monstrification process, with both union leaders and online participants eventually interpreting it as a disharmonious and uncontrollable phenomenon led by illegitimate actors. Union leaders then come to believe they have no option other than to ban the ‘trolls’ to keep the digital beast they have created at bay.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".