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Record W2604294466 · doi:10.1111/jacf.12223

Separating Leadership from Pay

2017· article· en· W2604294466 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of applied corporate finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsTyingCommitIncentiveBusinessMarketingPerformance managementPerformance indicatorSkepticismPay for performanceCompensation (psychology)Profit sharingProcess (computing)Process managementEconomicsFinanceMicroeconomicsComputer science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.125
GPT teacher head0.257
Teacher spread0.132 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it