Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it