Bibliographic record
Abstract
This program of research examines the perceptions of the flows of learning in a Canadian granting council during its renewal initiative. This case study will answer the following research questions: How do employees of a granting council perceive the flow of learning during the organization's transformation into a knowledge council? More specifically, how do they perceive the feed-forward flow between the individual and the group, and the feedback flow between the organization and the individual? Based on Crossan, Lane and White's (1999) 41 Framework and on the organizational learning, organizational change and social construction literatures, this qualitative study sheds light on two problematic relationships that may lead to tension at the junctures of the flows between the individual and the group, and the organization and the individual. Face-to-face, semi-structured interviews were conducted with 15 participants, and data was analyzed using Miles and Huberman's (1994) methods for interpretation. The data revealed that those who are heavily involved in the renewal conceptualization retain the learning that occurs during organizational changes. It also suggests that some factors consolidated under the constructs of capabilities/capacities, skills, roles and infrastructure, could be considered as indicators of the flows of learning. The findings confirm that middle management has a fundamental role in amplifying knowledge and in engaging and integrating employees. Preliminary results also show that routines are mostly at the individual and group levels, and that along with hierarchy, do not obstruct the flow of learning as much as is commonly believed. This research contributes to theory by studying the learning component during a renewal exercise, by building on the 41 Framework, by providing empirical data, and by extending research on organizational learning in federal granting agencies. Its implications for practice are that the mechanisms to spread organizational learning need to be taught, and that organizations could be considered as neutral rather than benevolent organisms where power is not malevolent, but residing in a set of relationships that employees have to navigate. The fields of education and management may benefit from the narratives that this research provides regarding the learning needs of knowledge workers. Keywords: Feed-forward and feedback flows of learning; tension; granting councils; organizational learning; organizational change.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".