Military Unionism and the Management of Employee Relations within the Armed Forces: A Comparative Perspective
Bibliographic record
Abstract
Many find the prospect of military unions totally inimical to the nature and functioning of the armed forces. Yet, a number of countries allow some form of military unionism, while others vehemently resist any form of independent union based on the premise that this undermines discipline, cohesion, and loyalty. This article examines how four different countries – the United Kingdom, Canada, South Africa, and Germany – have dealt with the issue of military unionism. The British Armed Forces, like many other English-speaking countries, have tended to approach employee relations from a typically unitarist position, which translates into union suppression or avoidance. The Canadian Armed Forces opted to circumvent the need for a military union by adopting a more human relations or neo-unitarist approach to employee relations. In South Africa, the military has been obliged by legal decree to accept a more pluralist dispensation, which has led to an overtly confrontational employment relationship. In Germany, where a union-like professional association exists, the approach has been more cooperative, even corporatist, typifying the European experience and philosophy towards unions, even in the military. In analysing the management of employee relations from these different typologies, the implications of union avoidance and acceptance within the armed forces are evaluated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".