Bibliographic record
Abstract
Performance management and incentive systems can play an important role in shaping a company's culture and promoting internal collaboration. Yet, in an uncertain and rapidly evolving world that rewards organizations for agility, performance management systems based on a single individual overall rating are being viewed with growing skepticism; and the once common practice of tying pay directly to such ratings is being reconsidered—and in many cases abandoned. But when carrying out this process of “separating leadership from pay,” companies must commit to providing employees with extensive ongoing feedback, as well as significant opportunities for development and growth that are not linked directly to financial rewards. In place of traditional bonus schemes whose payoffs are tied to individual performance measures, the authors also recommend the use of company‐wide bonus plans—similar in spirit to the General Motors plan described earlier in this issue—that reflect a philosophy of “sharing success” that aims to encourage and reinforce a culture of collaboration and agility. But for compensation plans built around sharing success to be effective, careful attention should be given to the “quality” of the results achieved. This can be accomplished by supplementing the use of Key Performance Indicators—such as, for example, economic profit—with the use of so‐called “boundary” KPIs—such as the percentage of satisfied clients—for which a minimum threshold must be met.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".