Bibliographic record
Abstract
Employment downsizing, the planned elimination of positions or jobs, is a defining characteristic of modern life in organizations. It may be reactive (in response to a change in economic or organizational conditions) or proactive (executed in anticipation of such changes). In the most recent economic recession, downsizing was global in scope, with 8.5 million layoffs in the United States and more than 50 million worldwide. As Datta, Guthrie, Basuil, and Pandey (2010) have noted, in these turbulent economic times even countries that traditionally have avoided layoffs (e.g., South Korea, Japan, Taiwan, and Hong Kong) embraced the practice. Export-oriented and labor-intensive firms in China, and firms in both manufacturing and services industries in Britain, Canada, Australia, New Zealand, South Africa, South America, and Eastern Europe participated as well. Not surprisingly, therefore, employment downsizing has attained the (dubious) status as one of the most high-profile, significant, and pervasive management issues of our time. Over the past three decades, downsizing has occurred in virtually all industries and sectors of the economy, and it has affected business, governments, and individuals around the world (Cascio, 2010a; Gandolfi, 2008). Although employment downsizing is a multifaceted phenomenon, characterized by antecedents, implementation, and consequences, this chapter addresses just three issues: what it is, what causes it, and some things we still do not know (i.e. directions for future research). The chapter does not consider other aspects of downsizing, such as its costs, consequences, or alternatives to it, that provide a more complete picture of the full scope of this phenomenon. For more on those issues, see other chapters in this volume or consult Cascio (2002, 2010), Datta et al . (2010), or De Meuse, Marks, and Dai (2011).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".