Introduction to the Special Issue ‐ The internet, social media and trade union revitalization: Still behind the digital curve or catching up?
Bibliographic record
Abstract
Abstract This article introduces the special issue of New Technology, Work and Employment titled “The Internet, Social Media and Trade Union Revitalization: Still Behind the Digital Curve or Catching Up?” The objectives of this special issue are threefold. First, to develop an analytical framework that can help researchers assess the role that internal and external factors play in mediating the nature and scope of union experimentation with new information and communication technologies (ICTs) and its contribution to the outcomes of revitalisation. Second, to present methods and concepts that are new to this area of research. Third, to generate empirical insight into how the various actors that constitute the trade union movement (e.g. worker councils, union confederations, trade unions, and union‐led coalitions) can and are using the internet, social media and artificial intelligence as a means of revitalisation. Taken together the geographical scope of the articles range from single‐country cases studies in Germany, the UK and Canada, to a cross‐national case study in Australia and the USA, and a comparative study across Europe. In terms of ICTs, attention is given to websites, Twitter, Facebook, YouTube and an AI chatbot.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.035 | 0.010 |
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".