Organizational Learning Facilitators in the Canadian Public Sector
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Organizational learning (OL) is considered to be a central element in the renewal of Canada's federal public service. What factors facilitate OL in this sector? How can these factors be measured? This study aims to answer these questions by describing the development of an instrument designed to produce a valid measure of the organizational learning facilitators (OLFs) relevant to public sector organizations. The confirmatory analysis indicated a 6-factor solution with 5 first-order factors ("knowledge acquisition and transformation," "learning support," "earning culture," "learning leadership, and "strategic management") and one second-order factor ("learning environment"). Results indicate that the OLF measure is a significant predictor of organizational outcomes. Keywords: organizational learningfacilitatorsexecutivespublic sectorCanadaconfirmatory factor analysis Notes 1The qualitative approach is phenomenological in the sense that emphasis must be placed on the actors' experience in order to prevent researchers from imposing their own a priori scheme of reference in the emergence of information (CitationGlaser & Strauss, 1967). 2The EX occupational classification represents to the highest levels of management in the Canadian public service. 3The following questions were asked: a. Describe in your own words what OL consists of?b. Could you illustrate this point with a specific example?c. What are the prerequisites for OL in your organization?d. In your opinion, what are the main facilitators of OL in your organization?e. From an individual and organizational standpoint, what are the factors confirming that OL has taken place in your organization? 4NVivo is a qualitative data analysis (QDA) computer software package produced by QSR International. It has been designed for qualitative researchers working with very rich text-based and/or multimedia information, where deep levels of analysis on small or large volumes of data are required. 5The experts familiar with the OL concept consisted of 5 professors in an OL research project, one consultant involved in introducing OL into a public organization, and 3 senior managers familiar with the public service. 6CVR = (n – N/2)/N/2 where n = the number of panellists who rated the item as 1 or 2 and N the total number of respondents. This procedure uses a majority vote to validate a given item's content. The CVR coefficient is interpreted as a correlation index. 7EFA is a statistical technique to achieve data reduction through a series of statistical parameters in order to achieve a sound representation of a given latent construct either through a simple (undimensionality) or more complex (multidimensionality) configuration of its constitutive items. 8For practical purposes, very small multivariate kurtosis values (e.g., less than 1.00) are considered negligible, while values ranging from 1 to 10 often indicate moderate non-normality. Values that exceed 10 indicate severe non-normality. 9Although this study does not specifically validate this scale of measurement, it is important to note that we have taken care of prior assessment parameters to measure of this instrument's psychometric properties. As such, the results observed following the confirmatory factor analysis indicate a good fit to the data (GFI = 0.95, CFI = 0.95, TLI = 0.94; RMSEA = 0.06). For these measurement scales, coefficients of internal consistency (Cronbach's alphas) obtained are 0.83 (individual learning), 0.80 (group learning), 0.85 (organizational impacts). 10 CitationBapuji and Crossman (2004) emphasize the need for more qualitative studies on OL in order to produce theories based on the reality of the environments involved. Only two empirical studies out of the 55 listed by these authors combined both qualitative and quantitative approaches in researching the OL phenomenon.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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