Introduction to the Special Issue ‐ The internet, social media and trade union revitalization: Still behind the digital curve or catching up?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it