A General Framework for Estimating Multidimensional Contingency Fit
Bibliographic record
Abstract
This paper develops a framework for estimating multidimensional fit. In the context of contingency thinking and the resource-based view of the firm, there is a clear need for quantitative approaches that integrate fit-as-deviation, fit-as-moderation, and fit-as-system perspectives, implying that the impact on organizational performance of series of bivariate (mis)fits and bundles of multiple (mis)fits are estimated in an integrated fashion. Our approach offers opportunities to do precisely this. Moreover, we suggest summary statistics that can be applied to test for the (non)significance of fit linkages at both the disaggregated level of individual bivariate interactions, as well as the aggregated level of groups of multivariate interactions. We systematically compare our approach with extant alternatives using simulations, including the fit-as-mediation alternative. We find that our approach outperforms these established alternatives by including fit-as-moderation and fit-as-deviation as special cases, by being better able to capture the nature of the underlying fit structure in the data and by being relatively robust to mismeasurements, small sample sizes, and collinearity. We conclude by discussing our method's advantages and disadvantages.
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.
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 | 0.074 | 0.210 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".