Managing Normative Tensions within and across Organizations
Bibliographic record
Abstract
What can the Economies of Worth (EW) and paradox frameworks learn from each other? Organizational paradoxes often present a moral dimension that has rarely been accounted for empirically or theorized by paradox scholars. The EW scholars, on the other hand, have developed a sophisticated analysis to explain how ordinary actors engage with multiple moral dimensions yet have barely theorized the full set of responses that actors can mobilize to deal with such tensions. This chapter addresses this double blind spot by cross-fertilizing paradox thinking and the EW framework with the aim of discussing the normative dimensions of paradox management and providing a conceptualization of how organizational actors can deal with tensions involving moral values. The EW framework is introduced, its assumptions with paradox approaches clarified and compared and then what each framework can learn from the other is analyzed. A research agenda is offered based on a new integrative framework.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.024 | 0.042 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".