Bibliographic record
Abstract
About a decade ago I coauthored a book on workplace commitment (Meyer & Allen, 1997). When I mentioned this project in conversation with a senior executive, I was somewhat taken aback by his reply: “I guess it will be a short book.” It turned out that this was becoming a fairly widespread sentiment that I had perhaps overlooked or denied as a result of my own enthusiasm for the topic. The major threat to commitment was change. Among other things, changes in technology, global competition, and consumer demands were placing pressure on organizations for improved efciency. Organizations responded in a variety of ways, many of which involved the elimination of jobs (e.g., mergers and acquisitions, downsizing, outsourcing, reengineering). In a review of downsizing trends, Emshoff (1994) cited a Conference Board survey in 1992 indicating that 90% of large corporations in the U.S. had downsized in the preceding ve years. He further noted that several companies had downsized more than once in a given year. Reichheld (1996) observed that many such downsizings occurred among companies that were protable at the time. Indeed downsizing had become a corporate strategy that was often rewarded by overnight increases in stock price.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".