1 THE ORGANISING MODEL IN AUSTRALIA:
Bibliographic record
Abstract
Throughout the Anglo-Saxon world the ‘organising model ’ has become the key union strategy for reversing membership decline. This article, however, argues that this model is conceptually flawed, in that it overlooks the significance of structural factors and strategies directed towards the regulation of occupational labour markets. In the absence of a system of industry or occupation-wide regulation even the best organised workplaces are exposed to de-unionisation. Sixteen years after its Australian adoption there is little evidence that the organising model has had any meaningful impact. ______________ The diminished influence of trade unions, manifested most visibly in declining union density, is one of the most significant industrial relations issues of our time. In Australia, 49 percent of the workforce belonged to a union in 1982. Twenty-five years later, only 18.9 percent held a union ticket (ABS 2008a). By way of comparison, in 2007 a mere 12.1 percent of the United States ’ workforce was unionised. In 1975, 28.5 percent had been union members (Bureau of Labor Statistics 2008). In Canada, union members comprised 29.7 percent of the workforce in 2007, compared to 38
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".