Digital unionism as a renewal strategy? Social media use by trade union confederations
Bibliographic record
Abstract
Traditional actors such as trade unions are inevitably challenged by digital technologies, not only from the perspective of labor relations, but also in relation to outreach and communications strategies. In fact, as online and offline realities become increasingly intertwined, the presence of organized labor institutions within the Internet’s current networked environment is unavoidable. This article debates digital trade unionism as a strategy for trade union renewal, particularly the implications of using social media platforms to connect and interact with a broader audience beyond the labor movement. Through a comprehensive comparative analysis of the Facebook pages of six trade union confederations from Brazil, Canada, Portugal, and the UK, we find that despite the possibilities for horizontal dialogue enabled by the new digital communication and information technologies, trade union confederations maintain an outdated ‘one-way’ model of communication, hindering opportunities to reach and engage with both union and non-union actors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".