Bibliographic record
Abstract
This paper analyzes the literature on industrial citizenship and unpacks its meaning through a process of categorisation. These categories are, aspirational citizenship, explanatory citizenship, welfare citizenship and global citizenship. The writings of Sidney and Beatrice Webb, Harold Lasky, and William B. Forebath represent aspirational citizenship. Explanatory Citizenship is gleaned from the labour law literature from Australia, the United States and Canada which has sought to explain the operation of their labour relations systems. If read broadly, these writers can be impliedly taken to endorse an approach to a type of industrial citizenship suitable for their nation. Welfare citizenship is explained through an examination of the seminal writings of T. H. Marshall and also through the recent work of Hugh Collins. In comprehending global citizenship, the writing of Linda Bosniak is examined. It is suggested that all writers on industrial citizenship argue that workers should receive fair wages and reasonable terms and conditions of employment, including protective legislation in the areas of unfair termination, privacy and occupational health and safety. More interesting, however, is the notion that industrial citizens should be given an input in to the processes of employer decision-making. It is also obvious that a re-working of industrial citizenship is essential to take account of the needs and aspirations of immigrant workers, of part-time and casual employees, of home workers, of labour hire employees, and of those workers who are receiving remuneration as independent contractors and consultants.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.021 | 0.015 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| 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".