Person-Centered Methodologies in the Organizational Sciences
Bibliographic record
Abstract
The 2011 Organizational Research Methods Feature Topic on latent class procedures has helped to establish person-centered analyses as a method of choice in the organizational sciences. This establishment has contributed to the generation of substantive-methodological synergies leading to a better understanding of a variety of organizational phenomena and to an improvement in research methodologies. The present Feature Topic aims to provide a user-friendly introduction to these new methodological developments for applied organizational researchers. Organized around a presentation of the typological, prototypical, and methodologically exploratory nature of person-centered analyses, this introductory article introduces seven contributions aiming to: (a) clarify the meaning, advantages, and applications of person-centered analyses; (b) illustrate emerging prototypical and longitudinal cluster analytic approaches; (c) introduce researchers to multilevel person-centered analyses as well as to auxiliary approaches that will drastically increase the scope of application of these methods; and (d) describe the application of these methods for confirmatory purposes.
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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 | 0.065 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".