Bibliographic record
Abstract
Introduction Scholars started inquiring into the trend of unionization around the early 1900s. John Commons is considered a pioneer who studied unionization trends around the early 1900s and tried to predict patterns of growth and decline of unionization (Ebbinghaus & Visser, 1999). It was furthered by Ashenfelter, John Pencavel, Bain and Elsheikh mainly in the British and US contexts. These scholars for a long time believed that unionization trends followed the business cycles in industry. When the industry flourished, there was a growth in the unionization and vice versa. It has been established beyond doubt that globally, unionization has been on the decline. The debate about the exact year of decline continues but there is a broad consensus amongst scholars of industrial relations that unionization has been on the decline since late 80s around the world (Checchi & Visser, 2005; Ebbinghaus & Visser, 1999; Visser, 2003; Western, 1997). Pattern of union growth and decline was predominantly studied on the basis of the effect of cyclical factors like unemployment and inflation on the unionization along with factors like major disturbances or a catastrophe and real wages (Ashenfelter & Pencavel, 1969; Bain & Elsheikh, 1976). This strand of research on the patterns of growth and decline of unionization has moved since then from UK & US and has been studied in other economies and geographies like Germany, Scandinavian countries and Canada. The scope of the research has expanded to include structural, economic and sociopolitical factors along with cyclical factors. It is also important to note that the trade unions are actors embedded within the social structure (Polanyi, 1957 for embeddedness). While trade unions perform the important function of representing the workers, the geographical context within which they are located leads to some differences (Herod, 2002; 2003). Hence viewing union decline through the lens of labor geography would nuance our understanding. The aim of our paper is to present integrated view of the factors that have led to decline in unionization, established in literature based on geographies. Since there are differences in connotations about unionization and union practices, we first clarify and define the scope of unionization. We present our analysis of the factors that have led to decline in unionization in terms of geographies and specific to some countries namely the USA, Britain, Germany, India, and China. Finally we discuss the implications and future research possibilities. Definition & Scope of Unionization Since there are wide differences in the understanding of unions and their activities, which could lead to different connotations of unions and unionizations in different geographies (Visser, 2006), we present a definition of Union and the scope of the union activities. For the purpose of this paper union has been defined as, an organization of employees which seeks to represent the job interests of its members to employers and in some circumstances to the state, but which is not dominated by either of them (Bain & Price, 1980: 2). Unionization can be defined in terms of union membership, union density, union activities and union power. Union membership is simply a dichotomous variable in the form of yes or no. Union Density is the ratio of unionized workforce to the total workforce. Net union density is the ratio of current unionized workers who are working and total workforce while gross union density is the ratio of unionized workers including retired and unemployed individuals who are drawing unemployment benefits and the total workforce. Union density has been used as a proxy for union power in studies (Addison, Bailey, & Siebert, 2003). Union activities include involvement in activities like strike, collective bargaining and worker representation. Studies however use strike action and collective bargaining settlement as a way of operationalizing union activities. …
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".