Relational incentives theory.
Bibliographic record
Abstract
Our life is built around coordinating efforts with others. This usually involves incentivizing others to do things and sustaining our relationship with them. Using the wrong incentives backfires: it lowers effort and tarnishes our relationships. But what constitutes a "wrong" incentive? And can incentives be used to shape relationships in a desired manner? To address these and other questions, we introduce relational incentives theory, which distinguishes between two aspects of incentives: schemes (how the incentive is used) and means (what is used as an incentive). Prior research has focused on means (e.g., monetary vs. nonmonetary incentives). Our theory highlights the importance of schemes, with a focus on how they interact with social relationships. It posits that the efficacy of incentives depends largely on whether the scheme fits the relational structure of the persons involved in the activity: participation incentive schemes for communal sharing relations, hierarchy for authority ranking relations, balancing for equality matching relations, and proportional incentive schemes for market pricing relations. We show that these four schemes encompass some of the most prevalent variants of incentives. We then discuss the antecedents and consequences of the use of congruent and incongruent incentive schemes. We argue that congruent incentives can reinforce the relationship. Incongruent incentives disrupt relational motives, which undermines the coordinating relationship and reduces effort. But, importantly, incongruent incentives can also be used intentionally to shift to a new relational model. The theory thus contributes to research on relational models by showing how people constitute and modulate relationships. It adds to the incentives and contracting literatures by offering a framework for analyzing the structural congruence between incentives and relationships, yielding predictions about the effects of incentives across different organizational and individual-level contexts. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; both teacher heads agree on what is shown here.
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".