The Domains of Organizational Learning Practices: An Agency-Structure Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it