Bibliographic record
Abstract
Classification is an important activity that facilitates theory development in many academic disciplines. Scholars in fields such as organizational science, management science and economics and have long recognized that classification offers an approach for ordering and understanding the diversity of organizational taxa (groups of one or more similar organizational entities). However, even the most prominent organizational classifications have limited utility, as they tend to be shaped by a specific research bias, inadequate units of analysis and a standard neoclassical economic view that does not naturally accommodate the disequilibrium dynamics of modern competition. The result is a relatively large number of individual and unconnected organizational classifications, which tend to ignore the processes of change responsible for organizational diversity. Collectively they fail to provide any sort of universal system for ordering, compiling and presenting knowledge on organizational diversity. This paper has two purposes. First, it reviews the general status of the major theoretical approaches to biological and organizational classification and compares the methods and resulting classifications derived from each approach. Definitions of key terms and a discussion on the three principal schools of biological classification (evolutionary systematics, phenetics and cladistics) are included in this review. Second, this paper aims to encourage critical thinking and debate about the use of the cladistic classification approach for inferring and representing the historical relationships underpinning organizational diversity. This involves examining the feasibility of applying the logic of common ancestry to populations of organizations. Consequently, this paper is exploratory and preparatory in style, with illustrations and assertions concerning the study and classification of organizational diversity.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".