Team-based employee remuneration
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
Purpose The purpose of this paper is to relate the balanced scorecard (BSC) to strategy and teams. Design/methodology/approach This paper proposes deriving performance targets and weights using a multiparty collaborative decision model that can be integrated into team-based bonus formulas. Findings Cross-functional division managers face a more complex problem in setting goals for individual managers. The proposed approach is intended to develop such goals and link them for team-based incentives. An example illustrates the application of the proposed BSC model and the team-based pay formula. Practical implications The model can be used to determine group bonus. Originality/value The paper has two objectives: to relate the BSC to the team setting with a participative flavor rather than with imposed targets and weights, and to develop a better way of relating behaviors and outcomes to the team’s and/or the organization’s goals. Integrating the strategies of various units adds a new dimension that differs from rationalizing the superior’s and the subordinate’s goals. The proposed model considers input from all value chain functional managers involved in implementing an organizational strategy. A methodology is provided to operationalize (Hope and Fraser, 2003) beyond the budgeting model principles.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.007 |
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