<b>Rethinking the concept of performance in strategy research: towards a performativity perspective</b>
Bibliographic record
Abstract
Organizational performance is an important concept in strategy research. In this paper, we interrogate the predominant focus on organizational performance as an aggregate organizational-level dependent variable and review three ways in which its role might be fruitfully reconsidered: (1) broadening consideration of performance to more disaggregated levels of analysis, (2) orienting research around the idea of performance as both input and outcome and finally (3) recasting performance in terms of performativity. We provide examples of research that has adopted each of these alternative approaches. We then examine the contributions and drawbacks of each perspective, before proposing an agenda for future research.
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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 | 0.063 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.078 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".