Bibliographic record
Abstract
Current theories of organizational learning that emphasize knowledge creation and transformation assume, but fail to problematize, the justification of belief in new knowledge, although they admit it is an essential enabling condition for the dissemination and integration of innovative ideas and practices into organizational practice. The present article aims to correct this imbalance by (1) developing a theoretical framework for the study of organizational justification of belief, and (2) reporting on an empirical study of the processes by which quality management progressively became accepted as a solution to the economic problems encountered by enterprise during the decade of the 1980s. Grounded in a constructivist view of knowledge, we demonstrate that what Nonaka and Takeuchi refer to as a managerial intention is in fact the product of a process of learning, involving the play of both external and internal influences, channeled through the continuing association and disassociation of interests that reflect the communities of practice and discourse that are typical of every complex organization. Justification of belief is thus both a social and a rhetorical accomplishment, whose outcome is a priori unpredictable.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.038 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".