Bibliographic record
Abstract
This symposium examines the question, ”Where are all the new management and organization theories?” This symposium: 1) briefly reviews a chronology of management and organization theories, 2) explores the reasons why there is a shortage of new theories, 3) examines the process of creating, building, publishing, and advocating new management and organization theories, and 4) explores ways to overcome the obstacles that inhibit the creation of new theories.Building Management and Organization TheoriesPresenter: Jeffrey Miles; U. of the PacificPersonal and Intellectual Roots in Building New TheoryPresenter: Jay B Barney; The Ohio State U.Origins, Twists and Turns, and Lessons Learned in Building New TheoryPresenter: Donald C. Hambrick; Pennsylvania State U.Revising What Constitutes a Theoretical ContributionPresenter: Dennis A. Gioia; Pennsylvania State U.Presenter: Kevin G. Corley; Arizona State U.Why a Shortage of New Theories and What We Can do About ItPresenter: David A. Whetten; Brigham Young U.Overcoming Challenges Facing Contemporary TheoristsPresenter: Roy R Suddaby; U. of Alberta
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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.018 | 0.035 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".