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
articles, “Downsizing of America, ” arguing that more intense competition and computer-based technological changes were inducing many companies to reduce costs and lay off workers, even ones with considerable seniority. Not surprisingly then, a recent study using the 1977 to 1996 U.S. General Social Sur-vey showed that during the 1990s, U.S. workers were more pessimistic than their counterparts in the 1980s about losing their jobs (Schmidt 1999). Since the mid-1990s, media reports of mass layoffs in large, often profitable companies have been common. Presumably, globalization has opened new market opportunities for some firms while confronting oth-ers with greater competition from abroad. In this con-text, many Canadians may ask whether they now face a greater chance than two decades ago of losing their job. Layoffs cause general uncertainty. For example, fami-lies with unstable earnings may need to change their consumption and savings patterns. Workers who can-not transfer their defined-benefit pension plans to other plans may find their retirement income affected. And displaced workers often require retraining. Job security can be viewed as a function of two com-ponents: the risk of layoff and the costs associated with layoff, measured by the earnings loss of displaced workers (OECD 1997). This article focuses on the first component, using the Longitudinal Worker file (LWF) to determine if permanent layoff rates rose between the 1980s and the 1990s (see Data source and concepts). But what were the chances of finding a new job in the event of a layoff? This issue is looked at by examining hiring rates and permanent quit rates during the same period. Data source and concepts The Longitudinal Worker File (LWF) is a 10 % random sample of all workers constructed from four sources: the Record of Employment (ROE) from Human Resources Development Canada (worker separations), the T1 (individual tax returns) and T4 (reported wages and sala-
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it