The Domains of Organizational Learning Practices: An Agency-Structure Perspective
Bibliographic record
Abstract
Background: Organizational learning theory has retained considerable attention in the past decades from a wide array of academic disciplines in social sciences. Yet few integrative efforts have satisfactorily offered a comprehensive and systematic articulation of the concept of organizational learning with regards to: (a) its core constitutive dimensions and associated mechanisms; (b) the analytical levels from such mechanisms operate (e.g., workers, teams, organizations); as well as (c) their interplay. Methods: This article builds on a critical synthesis of predominant approaches in organizational learning theory (i.e., structural functionalist, social constructivist and middle range approaches), highlighting the contributions of each approach on the key analytical elements guiding our inquiry (i.e., core dimensions and associated mechanisms, analytical levels, interplay). Drawing from the work of sociologists Anthony Giddens and Margaret Archer on agency-structure theory, we develop a series of theoretical propositions supporting the Organizational Learning Practices (OLP) concept as a unifying heuristic tool. Results: OLP are defined as a set of collectively shared practices held by members of a given organization embedded in normative, political, and semantic dynamics. At the heart of such dynamics lies organizational knowledge as a power resource pivotal to the sustainable development of organizations, as well as that of their members. Conclusion: OLP offer promising answers to on-going debates in organizational learning theory, and we conclude by discussing concrete guidelines to advance research and practice on OLP.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.053 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".